The boy was 3 years and 1 month old and had been healthy before admission. His intermittent abdominal pain had begun 38 hours earlier, with intermittent fever reaching 38.0 °C and seven to eight episodes of non-projectile, bilious vomiting. During the 10 hours before presentation, the pain intensified and shifted to the right side of the abdomen. He had not passed stool and denied diarrhoea or dysuria. He had no previous abdominal operation, trauma or transfusion, and there were no visible surgical scars. The absence of previous surgery was important because it excluded appendectomy as an explanation for the missing organ.

Examination showed moderate abdominal distension, marked right-sided tenderness, guarding and rebound tenderness. Bowel sounds were reduced to one or two per minute. Blood tests revealed leukocytosis with neutrophil predominance: the white blood cell count was 12.9 × 10⁹/L and the neutrophil count was 9.82 × 10⁹/L. C-reactive protein was elevated at 45.75 mg/L, while haemoglobin and platelet values were within paediatric reference ranges. These findings supported an acute inflammatory abdominal process, although they could not identify its cause or distinguish appendiceal agenesis from appendicitis.

Abdominal-pelvic computed tomography also failed to show the appendix. Instead, it revealed pericecal exudative changes and several enlarged mesenteric lymph nodes. The preoperative clinical impression was an acute abdomen, highly suggestive of acute appendicitis with localized peritonitis. The case illustrates an important limitation of imaging in this setting: a nonvisualized appendix is not diagnostic of congenital absence. The source article notes that inflammation, a retrocaecal position and technical limitations can all prevent an appendix from being seen on CT.

During laparoscopic exploration, the caecum was located in the right mid-abdomen and was attached to the lateral abdominal wall and right iliac fossa by adhesions. Surgeons lysed these adhesions and followed the taeniae coli toward their convergence point but identified neither an appendix nor a remnant. They then opened the retrocaecal peritoneum to expose the retrocaecal space fully, again finding no appendiceal tissue. A 2.5-centimetre extension of the abdominal incision allowed the ileocaecal segment to be exteriorized for direct manual palpation of the caecal wall.

That additional examination found no intramural or intussuscepted appendix, supporting the classification of the case as Collins Type III appendiceal agenesis. The operation also included retrograde inspection of approximately 1.5 metres of distal small bowel from the ileocaecal junction, excluding a Meckel’s diverticulum and other small-bowel malformations. Markedly enlarged mesenteric lymph nodes were observed, including one measuring 2.5 × 2.5 × 3 cm. Two representative specimens, one of them from the largest node, were taken for postoperative histopathology; no intraoperative frozen-section analysis was performed.

The five-part Collins system separates caeco-appendiceal developmental abnormalities by anatomy. Type I features complete absence of both caecum and appendix; Type II has a rudimentary caecum without an appendix; Type III, the category identified in this child, has a normal-appearing caecum with no appendix; Type IV has a normal caecum with a rudimentary or hypoplastic appendix; and Type V features a markedly dilated caecum without an appendix. Type III is described in the report as the most common phenotype in the available clinical literature, while the estimated incidence of appendiceal agenesis is roughly one in 100,000 surgical explorations for suspected appendicitis.

Confirming agenesis requires more than failing to find the appendix during surgery. Surgeons must review the patient’s operative history, mobilize the caecum and ascending colon, trace the taeniae coli, inspect the retrocaecal and ileocaecal regions, palpate the caecal wall and exclude a Meckel’s diverticulum. The investigation must also consider an intramural, intussuscepted, hypoplastic or ectopic appendix, along with appendiceal atresia, in which appendiceal tissue persists despite discontinuity of its lumen from the caecum. An ectopic appendix can occur in several unusual locations, although retrocaecal and pelvic positions are more common. The report emphasizes that a standardized exploration reduces the risk of mistaking an atypical appendix for true agenesis.

The precise cause of appendiceal agenesis remains uncertain. The report describes an embryonic process in which the appendix develops from a caecal diverticulum around the eighth week of intrauterine development. Disrupted development may result in complete absence. A leading hypothesis is an intrauterine mesenteric vascular accident between the seventh and twelfth weeks of gestation, causing ischaemic injury to the developing caecal diverticulum. Proposed influences also include teratogenic exposure, such as maternal thalidomide, and spontaneous intrauterine auto-amputation following torsion or inflammation. These mechanisms remain hypotheses and cannot be established from the clinical course of an individual child.

After the anatomical finding was confirmed, surgeons evaluated other possible explanations for the child’s acute-abdomen presentation. The authors considered intra-abdominal adhesions and reactive mesenteric lymphadenopathy the most likely contributors. This mattered because appendiceal agenesis alone does not account for the pain, fever, inflammatory laboratory results and pericecal changes that prompted surgery. The child received intravenous cefoperazone-sulbactam and metronidazole for infection management, together with fluid resuscitation and supportive care. Fever subsided gradually and abdominal discomfort resolved. On postoperative day 3, he passed flatus and stool, began clear fluids and progressed to a regular diet without vomiting or distension.

The surgical incisions healed primarily, and the boy was discharged on postoperative day 6 in stable condition while the permanent lymph-node histopathology remained pending. He was followed for three months and remained asymptomatic, without recurrent abdominal pain or fever; his abdominal examination was unremarkable. The final histopathological report identified reactive lymphadenopathy with no granulomatous inflammation or neoplastic lesions. As a single case report, the study cannot establish how often this combination occurs or whether the proposed mechanisms apply to other patients. Its practical value is narrower: when an appendix cannot be located, appendiceal agenesis belongs in the differential diagnosis, but it should be declared only after a thorough surgical search and assessment for other causes of acute abdominal pain.

Subject of Research: A paediatric case of appendiceal agenesis initially presenting with symptoms resembling acute appendicitis.

Article Title: Appendiceal agenesis in a 3-year-old boy presenting with suspected acute appendicitis: a case report

Article References: Yang, X., Wang, L., Zhang, D., & Yang, X. (2026). Appendiceal agenesis in a 3-year-old boy presenting with suspected acute appendicitis: a case report. Frontiers in Pediatrics, 14, Article 2012352. https://doi.org/10.3389/fped.2026.2012352

Image Credits: AI Generated

DOI: 10.3389/fped.2026.2012352

Keywords: appendiceal agenesis, paediatric surgery, acute appendicitis, appendix, ileocecal region, laparoscopy, mesenteric lymphadenopathy, case report, congenital anomaly, Appendiceal, agenesis, year-old

The research team set out to answer a deceptively simple question: once a clinician knows a child’s age, Pediatric Appendicitis Score, and C-reactive protein level, how much additional diagnostic value does an appendicitis-compatible initial ultrasound report actually provide? This question matters because ultrasound is widely used as the first-line imaging modality in children with suspected appendicitis, particularly given concerns about radiation exposure from computed tomography in young patients. Yet the incremental value of the initial ultrasound report, considered alongside established clinical variables, has remained surprisingly unclear in the literature. Prior studies have integrated ultrasound findings with clinical scores, refined sonographic criteria using age-based appendiceal measurements and secondary signs, and explored the meaning of partial appendix visualization, but few prospective cohorts have directly measured the statistical gain that ultrasound adds to a baseline clinical model.

To address this gap, the investigators conducted a prospective single-centre cohort study enrolling children aged 3 to 17 years who were evaluated for suspected acute appendicitis between May 2025 and March 2026. The primary outcome was histopathologically confirmed appendicitis, a rigorous standard that anchors the diagnostic analysis in tissue-level truth rather than clinical impression alone. The study design compared two statistical models. The first, a clinical model, included age, the total Pediatric Appendicitis Score, and log2-transformed C-reactive protein values. The second, an ultrasound-extended model, added an appendicitis-compatible initial ultrasound report to those same variables. Model performance was assessed using the area under the receiver operating characteristic curve, known as the AUC, along with the Brier score, likelihood-ratio testing, and the DeLong method for comparing correlated receiver operating characteristic curves.

The cohort comprised 135 children evaluated through an ultrasound-first pathway. Of these, 81 had histopathologically confirmed appendicitis, while 54 formed the comparison group of children in whom appendicitis was ultimately excluded. The clinical model alone demonstrated moderate apparent discrimination, achieving an AUC of 0.771 with a 95 percent confidence interval spanning 0.689 to 0.852, and a Brier score of 0.188. These figures describe a model that performs meaningfully above chance but still leaves substantial diagnostic uncertainty in individual cases, a familiar reality for clinicians who know that scores and inflammatory markers can overlap considerably between children who need surgery and those who do not.

The headline result emerged when ultrasound entered the equation. Among the 132 children whose initial ultrasound reports could be classified, the addition of an appendicitis-compatible ultrasound report increased the AUC from 0.772 to 0.912 and reduced the Brier score from 0.187 to 0.113. In practical terms, the AUC jump represents a substantial improvement in the model’s ability to separate children with confirmed appendicitis from those without it, moving discrimination from the moderate range toward levels typically associated with highly reliable diagnostic tools. The Brier score, which measures the mean squared difference between predicted probabilities and actual outcomes, fell by nearly 40 percent, indicating that the ultrasound-extended model produced not only better ranking of patients but also better-calibrated probability estimates. The likelihood-ratio framework confirmed that the improvement was statistically meaningful rather than a product of chance variation within the sample.

These numbers carry weight for anyone who has followed the evolution of pediatric appendicitis diagnostics. The Pediatric Appendicitis Score, originally described by Samuel in 2002, combines clinical findings such as migration of pain, anorexia, fever, and rebound tenderness into a structured assessment. C-reactive protein, an acute-phase reactant measurable within hours, has been validated through systematic reviews and meta-analyses as a useful but imperfect marker of inflammation in pediatric appendicitis. Previous work by Bachur and colleagues in 2015 demonstrated the value of integrating ultrasound findings with clinical scores, and subsequent studies have refined sonographic criteria by combining age-based appendiceal size thresholds with secondary findings such as periappendiceal fluid and echogenic fat. The new study extends this lineage by prospectively quantifying, within a single cohort, the exact statistical increment that a clear ultrasound report delivers on top of those established variables.

The study also examined the role of post-ultrasound computed tomography within the same ultrasound-first pathway. CT was performed in 52 of the 135 children, representing 38.5 percent of the cohort. Among those children, 29 had appendicitis, while 23 did not undergo appendectomy. This figure illustrates a persistent tension in pediatric imaging strategy: ultrasound is favored as the initial modality because it avoids ionizing radiation, but equivocal or negative ultrasound findings in children with persistent clinical suspicion often prompt a second-line CT examination. The ACR Appropriateness Criteria for suspected appendicitis in children endorse this staged approach, and prior implementation studies have shown that ultrasound and fast magnetic resonance imaging pathways can reduce CT utilization. The new data suggest that when the initial ultrasound report is clearly appendicitis-compatible, its diagnostic contribution is strong enough to anchor clinical decisions, while the residual group with inconclusive imaging continues to require careful escalation.

Methodologically, the choice of log2-transformed C-reactive protein reflects the skewed distribution typical of inflammatory markers, where a small number of children with very high values would otherwise dominate a linear model. The use of the DeLong comparison, a standard method for testing whether two correlated AUCs differ significantly, strengthens the inference that the ultrasound-extended model genuinely outperforms the clinical baseline rather than merely appearing to do so in this particular sample. The Brier score decomposition further distinguishes discrimination from calibration, two properties of a predictive model that can diverge: a model may rank patients correctly yet assign probabilities that are systematically too high or too low. The observed reduction from 0.187 to 0.113 indicates improvement on both fronts, which is precisely what clinicians need when translating a statistical model into bedside decisions about observation, imaging escalation, or surgical consultation.

The authors frame their conclusion carefully: a clear appendicitis-compatible initial ultrasound report provided substantial incremental diagnostic information when added to age, Pediatric Appendicitis Score, and C-reactive protein, supporting the role of ultrasound as an integrated component of the diagnostic assessment rather than a standalone test. This framing resists the common temptation to treat any single test as definitive. Appendicitis in children remains a condition in which history, physical examination, laboratory data, and imaging each contribute partial information, and the art of diagnosis lies in combining them coherently. The study’s prospective design, histopathological gold standard, and formal statistical comparison give its central claim unusual solidity for a single-centre cohort.

The implications reach beyond the radiology suite. For emergency physicians and pediatric surgeons, the results support protocols in which ultrasound findings are interpreted in the context of validated scores and inflammatory markers, rather than in isolation. For health systems weighing imaging investments, the data quantify the value of maintaining high-quality pediatric ultrasound services as the backbone of an ultrasound-first pathway. And for researchers, the study provides a template for measuring incremental diagnostic value with the rigor that the field increasingly demands. As pediatric appendicitis diagnostics continue to evolve, with standardized reporting templates, refined sonographic criteria, and alternative second-line modalities all under active investigation, this prospective cohort from Kütahya offers a clear quantitative answer to a question that clinicians have long asked in qualitative terms: ultrasound matters, and now the size of its contribution can be stated with confidence.

Subject of Research: Diagnostic value of ultrasound in pediatric suspected acute appendicitis

Article Title: Incremental diagnostic value of ultrasound in children with suspected acute appendicitis

Article References: Ibrahimoglu, H., & Sahin, S. (2026). Incremental diagnostic value of ultrasound in children with suspected acute appendicitis. Pediatric Radiology. https://doi.org/10.1007/s00247-026-06812-x

Image Credits: AI Generated

DOI: 10.1007/s00247-026-06812-x

Keywords: acute appendicitis, ultrasound, pediatrics, Pediatric Appendicitis Score, C-reactive protein, diagnostic accuracy, computed tomography, pediatric radiology, AUC, emergency medicine, histopathology, ultrasound-first pathway

The research team, led by Yunfeng Chen and Yunyun Rui of the Department of Ultrasound at Nanjing Gaochun People’s Hospital in China, assembled a cohort of 3,000 patients with acute appendicitis treated between January 2020 and December 2025. The primary cohort of 2,000 patients from Nanjing Gaochun People’s Hospital was split into a training set of 1,400 patients and an internal testing set of 600, while an independent external validation cohort of 1,000 patients was drawn from The Fourth Affiliated Hospital of Soochow University. Roughly six percent of patients across the cohorts developed septic shock, defined according to the Sepsis-3 consensus criteria as sepsis requiring vasopressor therapy to maintain a mean arterial pressure of at least 65 mmHg together with a serum lactate level above 2 mmol/L despite adequate fluid resuscitation. Crucially, the outcome was restricted to shock occurring before surgery or during conservative treatment, so the model predicts the natural progression of the infection rather than complications of the operation itself.

The pathophysiological rationale behind the model is elegant. The transition from a localized appendiceal infection to systemic shock is fundamentally rooted in the breakdown of local anatomical barriers, which triggers a cascade of immune hyperactivation, systemic inflammation, and metabolic decompensation. High-resolution abdominal ultrasound can capture direct morphological evidence of this destructive process: distinct sonographic features of the inflamed appendix can indicate irreversible transmural necrosis, which acts as the primary gateway for massive bacterial and endotoxin translocation into the systemic circulation. The researchers reasoned that combining these local imaging features with systemic immunological profiles derived from blood tests could yield a comprehensive multimodal predictive landscape that no single biomarker could provide.

Technically, the study began with a pool of 35 baseline variables extracted from electronic medical records, spanning demographics, vital signs, detailed appendiceal sonographic morphology, and extensive preoperative laboratory parameters. From the initial blood samples obtained within 24 hours of admission and strictly before surgery, the team mathematically derived four integrated indices designed to quantify the crosstalk between systemic immunity and metabolic nutrition. The Systemic Immune-Inflammation Index, or SII, is calculated by multiplying the peripheral platelet count by the absolute neutrophil count and dividing by the absolute lymphocyte count. The C-Reactive Protein to Albumin Ratio divides the serum CRP concentration by the serum albumin level. The Prognostic Nutritional Index sums the serum albumin level with five times the total peripheral lymphocyte count, and the Glucose-to-Lymphocyte Ratio divides the fasting blood glucose level by the absolute lymphocyte count. Rather than analyzing isolated cell counts, these composite indices reflect the delicate systemic equilibrium among aggressive hyperinflammation, host immune surveillance, and baseline metabolic reserves.

To distill this high-dimensional feature space into a parsimonious model, the researchers applied a two-step feature selection process exclusively within the training cohort. All continuous variables were Z-score standardized, and the Least Absolute Shrinkage and Selection Operator, or LASSO, regression with 10-fold cross-validation was used to address the severe multicollinearity among raw inflammatory markers, whose variance inflation factors exceeded 5 in the initial pool. Subsequent multivariable logistic regression identified six independent predictors: the Systemic Immune-Inflammation Index, the C-Reactive Protein to Albumin Ratio, the Glucose-to-Lymphocyte Ratio, the Prognostic Nutritional Index, periappendiceal fluid, and the sonographic loss of the submucosal echogenic layer. Notably, the final six predictors showed minimal correlation with one another, with a maximum Spearman coefficient of just 0.15 and variance inflation factors all below 2.0, ensuring minimal information redundancy in the optimal model.

Eight supervised machine learning algorithms were then constructed on these six variables, including Random Forest, eXtreme Gradient Boosting, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbors, LightGBM, and Naive Bayes. The Random Forest model demonstrated the highest discriminative performance, achieving an area under the receiver operating characteristic curve of 0.914 in the training set. Because septic shock was relatively uncommon, the team applied inverse-frequency class weights during model training and additionally evaluated the area under the precision-recall curve, which was 0.421 in the training cohort, 0.356 in the internal testing cohort, and 0.331 in the external validation cohort, supporting the model’s discriminatory ability after accounting for the low prevalence of the outcome. A sensitivity analysis using a uniform 48-hour prediction horizon yielded consistent discrimination across all three cohorts, with areas under the curve of 0.902, 0.886, and 0.878 respectively.

Validation was rigorous and three-tiered. After the final model was locked, it was applied directly to the internal testing and external validation cohorts without any refitting, feature selection, hyperparameter tuning, or recalibration. Calibration plots closely tracked the ideal 45-degree reference line across all three cohorts, with calibration intercepts near zero and slopes of 1.00, indicating generally good agreement between predicted and observed risks. Decision curve analysis demonstrated that using the model to guide clinical interventions yields a superior net clinical benefit compared to default treat-all or treat-none strategies across a broad spectrum of threshold probabilities. Importantly, the model significantly outperformed conventional clinical scoring systems: in the external validation cohort, the Random Forest achieved an area under the curve of 0.914 compared with 0.671 for qSOFA and 0.575 for the Alvarado score, with DeLong tests confirming the differences were highly significant.

To demystify the black-box nature of the algorithm, the researchers employed the SHapley Additive exPlanations, or SHAP, framework, which provides both a global perspective on feature importance and a local interpretation of individual patient predictions. The SHAP analysis identified the Systemic Immune-Inflammation Index as the paramount contributor to the model, followed by the loss of the submucosal echogenic layer, the C-Reactive Protein to Albumin Ratio, the Glucose-to-Lymphocyte Ratio, and the Prognostic Nutritional Index. Elevated systemic inflammatory indices combined with ultrasound signs of local anatomical destruction significantly increased the predicted probability of shock, while a robust immuno-nutritional status indicated by higher Prognostic Nutritional Index values exerted a substantial protective effect. The authors emphasize that these SHAP values reflect the contribution of predictors to model predictions and do not establish causality or statistical interaction between features.

The immunological interpretation of these findings is compelling. During early sepsis, peripheral neutrophils are massively recruited and activated, releasing reactive oxygen species and forming neutrophil extracellular traps, while platelets act as critical immune effectors by interacting with neutrophils to promote microvascular thrombosis, a hallmark of septic shock pathogenesis. Concurrently, sepsis induces rapid apoptosis of lymphocytes, leading to a state of profound adaptive immunosuppression. An elevated Systemic Immune-Inflammation Index therefore mirrors the lethal combination of hyperinflammation, microcirculatory dysfunction, and immune paralysis. Similarly, the C-Reactive Protein to Albumin Ratio quantifies the acute-phase response against hepatic synthetic function, serving as a sensitive barometer for the intensity of the systemic inflammatory cytokine storm, while an elevated Glucose-to-Lymphocyte Ratio reflects severe stress hyperglycemia coupled with infection-induced lymphopenia.

The sonographic predictors bridge the gap between systemic blood profiles and localized anatomical pathology. The submucosa of the appendix is rich in vascular and lymphatic networks, and the sonographic obliteration of this echogenic layer strongly indicates severe transmural ischemia, purulent micro-abscess formation, and impending or established gangrene. This irreversible structural destruction implies complete failure of the local mucosal barrier, providing a direct anatomical gateway for massive quantities of intestinal flora and endotoxins to translocate into the portal and systemic circulation. Periappendiceal fluid, the second ultrasound feature, similarly reflects local disease severity. The authors caution, however, that the present analysis does not establish a causal pathway, biological interaction, or synergistic effect between these systemic and local features, since formal interaction analyses were not performed.

To facilitate point-of-care clinical translation, the optimized model has been deployed as a publicly accessible web-based dynamic calculator built with the R Shiny package. Clinicians can input individualized patient profiles and obtain a real-time probability of septic shock, and the interface generates a patient-specific SHAP waterfall plot quantitatively detailing the exact contribution of each clinical and sonographic feature to the final prediction. Two representative clinical scenarios illustrate the tool’s capacity for personalized risk stratification: a low-risk patient with a predicted septic shock probability of 2.0 percent and a high-risk patient with a predicted probability of 59.0 percent.

Despite the promising results, several limitations warrant attention. The retrospective design carries a potential risk of selection bias, although strict consecutive enrollment and multicenter validation substantially mitigate this concern. The model relies on baseline parameters obtained strictly upon hospital admission, so the dynamic trajectories of these immune-inflammatory indices over time were not captured, which might provide additional prognostic insights during the initial resuscitation phase. Specialized immunological cytokines such as Interleukin-6 and Tumor Necrosis Factor-alpha were not routinely measured in the emergency setting and were therefore unavailable for inclusion. The limited number of positive events may also have contributed to uncertainty in the performance estimates, particularly in the external validation cohort, where the confidence interval for the Brier score was wider.

The authors conclude that this multicenter study developed and externally validated an interpretable machine learning model for early risk stratification of septic shock in acute appendicitis, and that the publicly accessible calculator may support individualized risk assessment. However, they stress that prospective impact evaluation is required to determine whether its clinical implementation facilitates earlier resuscitation or improves patient outcomes. Future studies incorporating dynamic biomarker monitoring and advanced multi-omics profiling are warranted to further refine this predictive framework. For now, the study stands as a vivid demonstration of how routine blood tests and bedside ultrasound, fused through transparent machine learning, may transform the early triage of a common surgical emergency.

Subject of Research: Machine learning model predicts septic shock in acute appendicitis using immune indices and ultrasound

Article Title: Systemic immune dysregulation and local barrier failure as predictors of septic shock in acute appendicitis: a large-scale multicenter interpretable machine learning study

Article References: Chen, Y., & Rui, Y. (2026). Systemic immune dysregulation and local barrier failure as predictors of septic shock in acute appendicitis: a large-scale multicenter interpretable machine learning study. Frontiers in Cellular and Infection Microbiology, 16, Article 1935756. https://doi.org/10.3389/fcimb.2026.1935756

Image Credits: AI Generated

DOI: 10.3389/fcimb.2026.1935756

Keywords: machine learning, septic shock, acute appendicitis, ultrasound, immune inflammation, predictive medicine, clinical decision support, Systemic, immune, dysregulation, local, barrier

The study, led by first authors Xiao Hu and Nian-Xia Qian under the correspondence of Xiao-Dong Liu, enrolled 49 patients with active, mild-to-moderate ileocecal Crohn’s disease and 78 patients whose acute appendicitis was confirmed by histopathology after surgery. Rather than relying on the subjective visual impression of inflammation, the team extracted precise measurements from each scan. These included the density of fat immediately surrounding the diseased bowel segment, the density of subcutaneous fat beneath the skin, the density of fat at the root of the mesentery, the attenuation of the psoas muscle in the lower back, the height of the peritoneal cavity, overall abdominal height, and four derived ratios labeled rCT1, rCT2, rCT3, and the peritoneal-to-abdominal height ratio, or PAR. Two radiologists measured every index independently and their values were averaged, a design that helps guard against the idiosyncrasies of any single observer.

The underlying logic is elegant. Fat density, expressed in Hounsfield units on the CT scale, rises when fat becomes inflamed and edematous, because fluid infiltration makes the tissue appear denser than healthy, airy adipose tissue. Muscle attenuation, meanwhile, reflects overall body composition and nutritional state, which chronic inflammatory diseases like Crohn’s can erode over time. Abdominal geometry, captured through peritoneal and abdominal height and their ratio, may reflect differences in body habitus and in the way disease redistributes tissue within the abdomen. By combining these simple, reproducible measurements, the researchers hoped to build a phenotypic fingerprint of each disease that no single scan reading could provide.

The results revealed genuine biochemical-visual differences between the groups. Perilesional fat density, the fat hugging the inflamed bowel, was lower in Crohn’s disease than in appendicitis, averaging minus 77.4 Hounsfield units versus minus 68.1. That counterintuitive direction suggests that the chronic, fibrofatty changes of Crohn’s disease, often described as creeping fat, alter the surrounding tissue differently from the acute, water-laden inflammation of a suddenly obstructed appendix. In contrast, the peritoneal-to-abdominal height ratio was higher in Crohn’s patients, at 0.55 versus 0.50, and psoas muscle attenuation was also higher, at 55.1 versus 50.1 Hounsfield units, hinting at systematic differences in body composition and abdominal architecture between the two populations.

To test whether these numbers could actually tell the diseases apart, the team built a three-index logistic regression model combining rCT2, PAR, and psoas muscle attenuation. In the two-group comparison, the model achieved an apparent area under the receiver operating characteristic curve of 0.880, with a 95 percent confidence interval of 0.82 to 0.94, and a leave-one-out cross-validated AUC of 0.854. Cross-validation matters here: by repeatedly holding out one patient and retraining on the rest, the LOOCV figure gives a more honest estimate of how the model behaves on unseen data than the apparent AUC alone. Still, the authors are careful to frame these values as quantifying phenotypic separation between the two groups rather than as diagnostic accuracy in a clinical sense, an important distinction that separates this study from the many overconfident machine-learning papers in the medical imaging literature.

Perhaps the most revealing analysis, however, was the one-class approach. Instead of comparing the two groups head to head, the researchers defined a 95 percent reference region using only the appendicitis patients, then asked how many Crohn’s patients fell outside that region. If the two diseases were truly distinct phenotypes, most Crohn’s cases should have landed outside. In reality, only 20.4 percent of Crohn’s patients, 10 of 49, fell outside the appendicitis-derived reference region, while just 2.6 percent of appendicitis cases, 2 of 78, did so. In other words, roughly four out of five Crohn’s patients looked, by these quantitative measures, statistically indistinguishable from appendicitis patients. That is a striking degree of overlap for two conditions with fundamentally different underlying biology, and it explains why visual differentiation on CT remains so difficult.

The team then stress-tested the transportability of their reference region using a separate cohort of 89 appendicitis cases, 59 from a different scanner at the same institution and 30 from an external center. Among the same-institution, different-scanner cases, 100 percent fell within the 95 percent reference region, a reassuring result for internal consistency. But among the external-center cases, only 80 percent, 24 of 30, fell inside. That 20 percent failure rate across centers is a familiar warning in radiology research: CT numbers are not universal constants. Differences in scanner manufacturer, calibration, reconstruction algorithms, and radiation dose can shift Hounsfield unit measurements enough to undermine models trained at a single site. No independent Crohn’s disease cohort was available for external validation, which the authors acknowledge as a key limitation.

Methodologically, the study is notable for its transparency and restraint. The researchers report following STROBE guidelines for observational studies and TRIPOD+AI guidelines for prediction models, and their supplementary material includes principal component analysis, hierarchical clustering, ROC curves, calibration plots, and sensitivity analyses. They also paid attention to the events-per-variable problem, a common pitfall in small clinical prediction studies where too many predictors are fit to too few outcomes, inflating apparent performance. By limiting their final model to three indices and reporting cross-validated rather than apparent performance, they avoided some of the most frequent statistical traps. The work was supported by the Natural Science Research Project of the Anhui Provincial Department of Education, and the ethics committee of Tongling Municipal Hospital approved the retrospective design with a waiver of written informed consent.

What does this mean for patients and clinicians today? The authors are explicit: the substantial overlap between the two conditions limits the use of these CT indices as a standalone discriminator, and the findings are exploratory and should not be interpreted as a clinically validated diagnostic tool. A patient with lower right abdominal pain should not expect a blood test or a scan measurement to reliably settle the Crohn’s-versus-appendicitis question anytime soon. Clinical context, laboratory findings such as white blood cell count, symptom duration, and follow-up imaging with CT enterography or magnetic resonance enterography remain the backbone of diagnosis. What the study does offer is a rigorous, honest baseline: it shows which quantitative features carry signal, how much they overlap, and how fragile such measurements can be when they cross institutional boundaries.

The broader significance lies in the study’s framing of a problem that will only grow as artificial intelligence enters radiology. Hundreds of papers each year report models that distinguish diseases on scans with impressive AUCs, yet few ask the harder question of whether the measured phenotype is stable across scanners, centers, and populations. By publishing a one-class reference analysis, an external-scanner cohort, and a candid account of failure modes, the Anhui team has modeled the kind of self-skeptical science that medical imaging needs. The creeping fat of Crohn’s disease and the fiery inflammation of appendicitis may never be fully separable by numbers alone, but knowing exactly where the boundary blurs is the first step toward tools that genuinely help the radiologist staring at that ambiguous lower right quadrant.

Subject of Research: Quantitative noncontrast CT indices of perienteric fat and abdominal geometry in ileocecal Crohn disease versus acute appendicitis

Article Title: Noncontrast CT indices of perienteric fat and abdominal geometry in ileocecal Crohn disease and acute appendicitis: an exploratory comparison

Article References: Hu, X., Qian, N.-X., Chang, L.-H., Ren, Y.-Q., Gao, T., & Liu, X.-D. (2026). Noncontrast CT indices of perienteric fat and abdominal geometry in ileocecal Crohn disease and acute appendicitis: an exploratory comparison. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02901-3

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02901-3

Keywords: Crohn's disease, acute appendicitis, computed tomography, fat stranding, perienteric fat density, Hounsfield units, psoas muscle attenuation, peritoneal-to-abdominal height ratio, radiology, diagnostic imaging, inflammatory bowel disease, cross-validation

The research team, led by Amr A. Elgharib and colleagues, assembled a cohort of 162 patients aged 15 and over who presented with abdominal pain and were clinically suspected of having appendicitis between March 2024 and March 2025. All of them underwent appendectomy, and the final word on their diagnosis came from histopathological examination of the removed appendix, the definitive reference standard. The cohort was deliberately enriched: roughly 70 percent of patients turned out to have confirmed appendicitis, a prevalence far higher than in a general emergency department, which shapes how the results must be interpreted.

The researchers fed ChatGPT-5 structured clinical narratives for each patient, including age, sex, symptoms such as fever, nausea, vomiting and right lower quadrant pain, physical examination findings like rebound tenderness, and laboratory values including white blood cell count and neutrophil percentage. The prompt cast the model as a general surgery physician in an emergency department and, crucially, forbade it from using any established appendicitis scoring system such as Alvarado, AIR, RIPASA or AAS. Instead, the model had to rely on its own clinical reasoning, outputting either a diagnosis of acute or non-acute appendicitis and, for positive cases, a percentage probability stratified into low, intermediate or high risk.

The study was designed in two phases following the classic machine learning convention of a 70-30 split. In Phase 1, 114 cases were presented without any prior exposure to labeled examples, simulating how a clinician might use a readily available chatbot off the shelf. After the model’s predictions were recorded, the true diagnoses were revealed, and in Phase 2 the remaining 48 cases were used to test whether this in-context learning improved performance. The prompt format was fixed across all cases, and the model’s memory was cleared before each evaluation round to test its default behavior.

The headline numbers are eye-catching. In Phase 1 without randomization, ChatGPT-5 achieved 100 percent sensitivity, meaning it caught every single case of appendicitis with zero false negatives, alongside 81.6 percent specificity, a positive predictive value of 91.6 percent, a negative predictive value of 100 percent, and overall accuracy of 93.9 percent. The Alvarado score, by contrast, managed only 67.1 percent sensitivity, missing 25 true cases, though its specificity was higher at 92.1 percent, and its overall accuracy came to 75.4 percent. Statistical comparison using McNemar’s test for paired proportions confirmed that the model’s sensitivity and accuracy advantages were highly significant, with p-values below 0.001.

But the story took a twist when the researchers examined a subtle methodological trap known as order leakage. When cases are presented in a sequence, a language model can inadvertently exploit the ordering of the data rather than genuine clinical reasoning, artificially inflating its apparent accuracy. To probe this, the team re-ran the evaluations with the case order randomized. Sensitivity barely budged, remaining at 98.7 percent in Phase 1, but specificity collapsed dramatically from 81.6 percent to 36.8 percent, a difference that was highly statistically significant. In other words, part of the model’s apparent precision in ruling appendicitis in may have been an artifact of how the cases were sequenced in the prompt.

Phase 2 told a more balanced story. After in-context learning, the non-randomized model reached 100 percent sensitivity, 93.8 percent specificity, 97.0 percent positive predictive value, 100 percent negative predictive value and 97.9 percent accuracy, statistically indistinguishable from the Alvarado score’s 96.9 percent sensitivity and 93.8 percent specificity. Agreement between the two methods, measured with Cohen’s kappa, climbed from a moderate 0.503 in Phase 1 to an almost perfect 0.952 in Phase 2. The authors note that in-context learning produced a modest, non-significant gain in specificity, while sensitivity was largely unaffected, suggesting the model’s core ability to exclude appendicitis was robust from the start.

The findings sit within a growing but cautionary literature. Previous evaluations have found that ChatGPT’s answers to surgeon-designed appendicitis management questions were clinically pertinent but inconsistent, and that ChatGPT-3.5 performed significantly worse than clinicians for some acute abdominal conditions such as cholecystitis and diverticulitis. A systematic review of 29 studies on artificial intelligence in appendicitis highlighted wide heterogeneity in inputs, validation strategies and metrics. Imaging-based machine learning models reading CT scans have achieved sensitivities around 77 percent and specificities around 86 percent. The new study’s authors also point to broader evidence that data leakage has inflated reported performance in fields ranging from Parkinson’s disease detection to brain MRI classification, reinforcing their insistence on randomization as a guard against over-optimistic estimates.

The authors are careful about what their results do and do not license. Because the cohort consisted exclusively of surgically confirmed patients with high pre-test probability, the estimates cannot simply be generalized to the unselected emergency department population in whom appendicitis must be ruled in or out, and the study was limited to a single center, a single model and a single comparator, with newer scores like AIR and RIPASA omitted because the required data were not consistently available. ChatGPT-5, they conclude, cannot yet be relied upon to confirm acute appendicitis, given the Alvarado score’s higher specificity, and the findings should be regarded as hypothesis-generating pending prospective validation.

What the study does establish is a proof of concept with real clinical texture: a general-purpose language model, guided by a carefully engineered prompt and given nothing more than the routine clinical data already sitting in a patient’s chart, can match or exceed a century-old scoring heuristic at the task that matters most for safety, excluding the disease. The authors envision ChatGPT-5 as a clinical decision-support tool to assist professionals during early assessment, particularly in resource-limited settings, always under physician supervision and never as an autonomous diagnostician. They also warn explicitly against patient self-diagnosis, noting that false reassurance from a chatbot could delay timely medical evaluation. As large language models continue their march into medicine, this Egyptian study offers both an encouraging data point and a methodological warning: before trusting an AI’s diagnostic brilliance, check whether the cases were shuffled.

Subject of Research: Diagnostic accuracy of ChatGPT-5 compared with the Alvarado score for acute appendicitis

Article Title: Diagnostic performance of ChatGPT for acute appendicitis compared with the Alvarado score: a retrospective diagnostic accuracy study

Article References: Elgharib, A. A., Ghanem, M. I., Ibrahim, R. S., Elwakeel, N., & Shemes, A. (2026). Diagnostic performance of ChatGPT for acute appendicitis compared with the Alvarado score: a retrospective diagnostic accuracy study. Discover Artificial Intelligence, 6(1), Article 1352. https://doi.org/10.1007/s44163-026-02332-7

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02332-7

Keywords: ChatGPT-5, large language models, acute appendicitis, Alvarado score, diagnostic accuracy, artificial intelligence, clinical decision support, histopathology, order leakage, in-context learning, emergency medicine, prompt engineering