The mitral valve sits between the left atrium and the left ventricle, and its two leaflets—anterior and posterior—must open and close tens of thousands of times a day without leaking. When the valve fails, surgeons may reconstruct the leaflets using a patch of pericardium harvested from the patient. The success of such reconstruction depends on getting the geometry right: the height, width, and free-edge length of each scallop of the leaflets determine how well the repaired valve coapts and seals during systole. Conventional proportional formulas, which estimate leaflet dimensions from a few reference measurements, provide only simplified approximations that ignore the considerable anatomical variability between individuals.

To address this gap, a team of researchers from I.M. Sechenov First Moscow State Medical University (Sechenov University) in Moscow and Universiti Malaysia Sarawak analyzed clinical and morphometric data from 72 adult autopsy cases in which the mitral valve was free of structural disease. From each case, they recorded a set of patient-level characteristics and valve-level measurements, then defined eight anatomical targets that would be required for building a leaflet template: heights and free-edge lengths of the individual anterior and posterior leaflet scallops. The posterior leaflet is typically divided into three scallops—P1, P2, and P3—while the anterior leaflet is similarly segmented into A1, A2, and A3 regions, and each of these segments has its own measurable geometry.

The researchers compared eight different regression approaches in separate target-specific modeling workflows rather than forcing a single algorithm onto all eight anatomical outputs. This design reflected an important practical insight: different anatomical dimensions may depend on different combinations of inputs and may exhibit different statistical relationships with the available predictors. After systematic comparison, the final model set consisted of four ElasticNet regressors and four random forest regressors, a split that balances the interpretability and regularization of penalized linear models against the flexibility of ensemble tree-based methods.

The clinically oriented configuration used 17 patient- and valve-level inputs to estimate the eight anatomical dimensions. Predictive performance varied considerably across targets. The most accurate predictions were achieved for the heights of the P1 and P3 scallops of the posterior leaflet, where the mean absolute error was between 1.68 and 1.70 millimeters and the coefficient of determination, R-squared, reached 0.54 to 0.55. In practical terms, the models could estimate these posterior scallop heights to within roughly a millimeter and a half to two millimeters of the true anatomical value, and they explained a moderate share of the variance across individuals. Among the anterior-leaflet parameters, the A3 height showed the best performance, with a mean absolute error of 2.33 millimeters and an R-squared of 0.21, indicating that while the average error remained clinically modest, the models captured much less of the individual-to-individual variability in this region.

The least predictable target was the free-edge length of the posterior leaflet, where the mean absolute error rose to 13.32 millimeters and the R-squared dropped to just 0.02—essentially no better than predicting the population mean. This result is anatomically meaningful rather than merely a modeling failure. The free edge of the posterior leaflet is a long, scalloped, highly variable structure, and its total length appears to depend on factors not well represented among the 17 inputs available in this dataset. The finding highlights a key limitation of the proof-of-concept stage: the models are only as informative as the morphometric and clinical variables they are given, and some dimensions of the valve may require additional predictors, such as imaging-derived measurements of the annulus, chordae, or ventricular geometry.

A central strength of the study lies in its clinical orientation. Rather than stopping at statistical performance metrics, the authors incorporated the model outputs into an interactive tool for generating individualized computer-aided leaflet templates. In the envisioned workflow, a surgeon would enter a small set of patient- and valve-level measurements, the trained models would predict the eight required anatomical dimensions, and the tool would translate those predictions into digital templates sized for the individual patient. Such templates could then be used to cut and shape an autologous pericardial patch with a precision that proportional rules cannot deliver, potentially reducing intraoperative guesswork and improving the geometric fidelity of the repair.

The authors are careful to frame the work as a proof of concept rather than a clinical tool ready for the operating room. The dataset of 72 anatomical cases, while sufficient to train and compare regression models, is modest by machine-learning standards, and the models were developed on autopsy-derived measurements rather than on living patients. Before any clinical implementation, the researchers emphasize that a prospective comparison of imaging-derived and anatomical measurements is required to confirm that predictions based on clinical imaging data align with true anatomical geometry, followed by validation in independent cohorts. These steps are essential to ensure that the millimeter-level accuracies observed in the anatomical dataset translate into real-world surgical reliability.

The study also carries implications for how patient-specific modeling may evolve in structural heart surgery more broadly. Individualized prediction of valve geometry fits into a larger trend toward computational planning in cardiac surgery and interventional cardiology, where three-dimensional echocardiography, computed tomography, and cardiac magnetic resonance imaging increasingly feed patient-specific models that simulate device behavior and repair outcomes. A machine-learning layer that estimates otherwise unmeasurable anatomical parameters could complement these imaging pipelines, particularly in settings where advanced imaging is incomplete or where the relevant dimensions are not directly accessible. The Sechenov and UNIMAS team’s target-specific approach—fitting and selecting a different model for each anatomical output—offers a pragmatic template for other groups attempting to map clinical predictors onto complex three-dimensional anatomy.

For now, the message of the study is one of careful, incremental progress. The models perform well for some dimensions, notably the heights of the P1 and P3 posterior scallops, but struggle with others, especially the posterior free-edge length, and the authors do not overstate their case. What they have demonstrated is that the full chain—from anatomical measurement, through machine-learning estimation, to digital template construction—is technically feasible, ethically approved, and open to refinement. If subsequent prospective and independent validation confirms the approach, the humble autopsy dataset could become the foundation for a new generation of personalized mitral valve reconstructions, in which the leaflet a surgeon implants is not an approximation drawn from population averages but a prediction tailored to the anatomy of a single patient.

Subject of Research: Machine-learning prediction of patient-specific mitral valve anatomical parameters for reconstructive planning

Article Title: Machine-learning prediction of patient-specific mitral valve anatomical parameters for reconstructive planning

Article References: Komarov, R. N., Dydykin, S. S., Vasalatiy, I. M., Kapitonova, M., & Drakina, O. V. (2026). Machine-learning prediction of patient-specific mitral valve anatomical parameters for reconstructive planning. BioMedical Engineering OnLine. https://doi.org/10.1186/s12938-026-01624-4

Image Credits: AI Generated

DOI: 10.1186/s12938-026-01624-4

Keywords: mitral valve, machine learning, morphometry, patient-specific prediction, autologous pericardium, reconstructive planning, ElasticNet, random forest, cardiovascular models, echocardiography, predictive medicine, biomedical engineering

The study, led by Yadigar Kastamoni and Kenan Öztürk of the Department of Anatomy at Süleyman Demirel University, together with Ahmet Dursun, Onur Can Şanlı and radiologist Veysel Atilla Ayyıldız, set out to reveal sex-related characteristics through morphometric measurements taken on the mandible, and to predict sex in individuals from different age groups using those variables. The work addresses one of the enduring challenges of forensic anthropology: identifying reliable, population-appropriate skeletal markers of sexual dimorphism that remain robust across the adult lifespan.

The research team analysed head and neck computed tomography images from 152 individuals, 77 males and 75 females, aged between 20 and 98 years. From these scans, the researchers generated three-dimensional reconstructions of each mandible using the RadiAnt DICOM viewer, a software platform that converts the two-dimensional slice data produced by CT scanners into volumetric models that can be measured in three dimensions. This approach reflects a broader shift in forensic science away from measurements taken with calipers on dry skulls and toward virtual anthropology, in which clinical imaging archives become rich sources of skeletal data.

The specific landmarks chosen for measurement were carefully selected regions of the mandible with well-established anatomical identities. The team measured dimensions relating to the foramen mentale, the mental foramen through which the mental nerve and vessels emerge on the front of the jaw; the condylus mandibulae, the rounded condyles that articulate with the skull at the temporomandibular joints; the incisura mandibulae, the notch between the condyle and the coronoid process on the upper margin of the ramus; and the angulus mandibulae, the angle of the jaw at the back and lower corner of the bone. In addition, the researchers measured the bigonial distance, the straight-line breadth of the jaw between the two gonion points at the angles on either side. All measurements, except those involving the mandibular angle, were carried out in RadiAnt DICOM Viewer 2020.1.1, and the resulting data were analysed statistically using SPSS 20.0 for Windows.

The results were strikingly consistent with the known biology of the human mandible. When the parameters were compared between the sexes, the mean values of all measurements, with the single exception of the left mandibular angle, were larger in males than in females. This pattern mirrors the influence of testosterone-driven bone growth during puberty, which produces a generally larger, squarer and more robust jaw in men, while hormonal and muscular factors in women tend to produce a jaw with a more obtuse gonial angle and finer proportions. The only measurement that failed to show a statistically significant sex difference was the gonial angle itself on the left side, a finding that reinforces a long-standing caution in the literature: angular measures of the jaw are notoriously variable and are influenced by age-related remodelling, tooth loss and chewing forces.

The degree of sexual dimorphism varied considerably across the measured parameters. The highest dimorphism rate was observed in the incisura mandibulae, the mandibular notch, which differed between the sexes by 14.39 percent on the right side and 14.29 percent on the left. At the opposite extreme, the angulus mandibulae showed almost no dimorphism, with differences of just 0.12 percent on the right and 0.57 percent on the left. This gradient is informative for forensic practice: it suggests that linear distances spanning the ramus, particularly between the mandibular condyle and the gonion, carry far more sex-diagnostic information than the angle of the jaw, which should be treated with caution when sexing fragmentary remains.

Indeed, the statistically significant differences between the sexes were found specifically in the distances from the condylus mandibulae to the gonion on both sides of the jaw, alongside the bigonial distance. These findings give forensic anthropologists a small set of hardy landmarks, robust bony points that survive decomposition and trauma relatively well, from which reliable linear measurements can be taken. Because the condyle and gonion are prominent structures on the ramus, they remain measurable even when the delicate alveolar portion of the jaw bearing the teeth has been damaged or lost, a common scenario in archaeological and forensic contexts.

What elevates the study beyond a simple descriptive anatomy exercise is its predictive modelling. The researchers built a logistic regression model from their measured variables, which correctly classified 91.4 percent of individuals in the development sample. A parallel discriminant function analysis achieved 90.1 percent classification accuracy on the original sample, and, crucially, retained 85.5 percent accuracy under leave-one-out cross-validation, a stringent test in which each individual is classified by a model trained on everyone else. The modest drop between original and cross-validated performance indicates that the model generalises reasonably well rather than simply memorising the training data, an important consideration for any method intended for real casework.

The authors conclude that sex estimation can be successfully performed using external measurements of the mandible, and they highlight the bigonial distance and the gonion-to-condyle measurements as particularly valuable parameters for sex differentiation. They suggest the method may prove useful in anatomy, forensic medicine and anthropology, especially in studies requiring sex estimation. This positions the mandible alongside the pelvis and skull as a first-rank bone for sex estimation, with particular value in contexts where those more classically dimorphic elements are missing. The mandible is also among the densest and most durable bones in the human skeleton, frequently surviving fire, fragmentation and long burial, which makes any validated method for reading sex from it disproportionately useful.

The study also carries broader implications for how forensic science will develop in the coming years. Computed tomography is now routinely performed in hospitals worldwide, and archives of clinical scans represent vast, untapped populations of reference data. Virtual osteometry of this kind does not require a physical skeleton at all: it can be applied ante-mortem to living patients in identification contexts, and post-mortem using clinical or forensic CT, avoiding destructive sampling of evidence. Earlier work cited by the team, including validation studies of three-dimensional CT measurements and postmortem CT morphometry of the mandible in Japanese populations, had already established the technical reliability of imaging-based measurement; the new study extends that framework with a large, wide-ranging adult sample spanning nearly eight decades of age, from 20 to 98 years.

As with all skeletal sex-estimation methods, population specificity remains the central caveat. The dimensions of the mandible vary across ancestral and geographic groups, and the classification functions derived here were developed on a Turkish sample; forensic practitioners applying them elsewhere would need to validate or recalibrate the model against local reference data before relying on it in identification casework. The retrospective design, approved by the Human Research Ethics Committee of Süleyman Demirel University with informed consent waived for anonymised data, also means the findings describe a clinical imaging population rather than a random demographic one. Nevertheless, with cross-validated accuracy approaching ninety percent from a handful of simple linear measurements, the message of the study is clear and, for a field where even a single additional clue can resolve an identification, quietly revolutionary: the jawbone remembers who you were, and it is willing to tell.

Subject of Research: Sex estimation from morphometric measurements of the human mandible in three-dimensional computed tomography images

Article Title: Sex estimation from the mandible in 3D computed tomography images

Article References: Kastamoni, Y., Dursun, A., Şanlı, O. C., Ayyıldız, V. A., & Öztürk, K. (2026). Sex estimation from the mandible in 3D computed tomography images. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-04001-x

Image Credits: AI Generated

DOI: 10.1007/s00414-026-04001-x

Keywords: sexual dimorphism, mandible, computed tomography, forensic anthropology, 3D imaging, morphometry, sex estimation, bigonial distance, logistic regression, discriminant analysis, human identification, osteometric analysis