Not All Collapsed Lungs Are Equal: Why AI Must Catch the Neonatal Pneumothoraces That Matter Most

When a deep learning model reports an impressive overall accuracy for detecting pneumothorax in newborns, the number can feel reassuring. A single percentage, however, conceals the question that keeps neonatologists and pediatric radiologists awake at night: which pneumothoraces did the model actually find, and which ones did it miss? In a Matters Arising contribution published in Pediatric Radiology, Didem Tatar, Ercan Yılmaz and Nezihe Köker Özer of Inonu University in Malatya, Türkiye, challenge the way the performance of an artificial intelligence system for neonatal pneumothorax has been framed, arguing that aggregate accuracy is the wrong yardstick for a condition in which clinical significance varies dramatically from one patient to the next.

The exchange centers on a study by Sanmoto and colleagues, published in the same journal on 12 September 2026, in which the researchers developed and evaluated a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs. Deep learning models of this kind are trained on large sets of labeled X-ray images, learning to recognize the visual signatures of air collecting in the pleural space around a lung. In adults, such tools have advanced rapidly, but the newborn chest presents a far harder problem: small lungs, thymic shadows, respiratory support devices, skin folds and atelectasis all conspire to create images where air collections and normal structures can be genuinely difficult to tell apart, even for experienced readers.

The Turkish authors’ reply, published on 8 October 2026, does not dispute the technical achievement of building such a model. Their concern is conceptual and clinical. A pneumothorax in a newborn is not a binary event with uniform consequences. Some air leaks are small, asymptomatic and resolve on their own under observation. Others are tension pneumothoraces, in which accumulating air shifts the mediastinum, compresses the contralateral lung, impairs venous return and threatens cardiovascular collapse within minutes. A detection system that performs well on the average case but stumbles on the tension physiology that demands immediate needle decompression would score respectably on paper while failing precisely where it matters most.

This is why the correspondence invokes the question in its title: does the model detect the neonatal pneumothoraces that matter most? Overall accuracy, the authors argue, blends together easy and hard cases, clinically trivial and clinically urgent findings, into a single figure that can mask systematic blind spots. In a dataset where small pneumothoraces outnumber large ones, a model could achieve high accuracy while missing a disproportionate share of the large, dangerous leaks. Conversely, sensitivity reported across all pneumothoraces tells clinicians nothing about sensitivity restricted to the subset requiring intervention. The metrics that matter for deployment are stratified ones, broken down by size, laterality, tension features and the patient’s clinical status.

The reply grounds this argument in the evolving evidence on how neonatal pneumothorax should be managed. Tatar and colleagues cite work by Baillie and colleagues, published in 2026 in the journal Children, which explored whether simple radiographic measurements can predict the need for intervention in neonatal pneumothorax. That line of research reflects a broader shift in neonatology toward tailoring treatment to severity rather than treating every radiographically visible air leak identically. If radiologists and neonatologists increasingly use measurements on the film to decide who needs a chest drain and who can be watched, then an AI system intended to support those decisions must be evaluated against the same clinically meaningful thresholds, not against a generic label of pneumothorax present or absent.

Management evidence reinforces the stakes. The authors also point to a randomized clinical trial by Murphy and colleagues, published in JAMA Pediatrics in 2018, which examined the effect of needle aspiration of pneumothorax on subsequent chest drain insertion in newborns. That trial is part of the movement toward less invasive first-line treatment for symptomatic air leaks in neonates, an approach that reduces drain placement in many infants. But selective, less invasive management depends entirely on accurate, timely characterization of the leak. If the imaging assessment that feeds into those decisions is delegated partly to an algorithm, the algorithm’s errors propagate directly into triage: a missed significant pneumothorax delays decompression, while a false alarm on an incidental small leak can trigger an unnecessary invasive procedure in a fragile premature infant.

The technical implications for model development are substantial. Training and validating a neonatal pneumothorax detector on binary labels alone invites the very failure mode the correspondence describes. A more clinically attuned pipeline would annotate lesions by size and tension features, weight the loss function toward the high-severity cases, and report performance separately for clinically significant versus clinically insignificant pneumothoraces. External validation on datasets from different scanners, different patient populations and different levels of prematurity would test whether the model generalizes beyond its development environment. Calibration, too, deserves attention: a model that outputs a probability should have that probability mean what it claims, because downstream triage thresholds are only meaningful if the underlying scores are trustworthy.

None of these caveats diminish the promise of computer-aided diagnosis in the neonatal intensive care unit. Chest radiography remains the first-line imaging modality for suspected air leak in newborns, and interpretation is subject to interobserver variability, fatigue and the time pressures of a busy unit. A well-validated AI system could serve as a second reader, flagging studies that need urgent review and reducing the chance that a tension pneumothorax sits unnoticed while a radiologist works through a queue. The point raised by Tatar, Yılmaz and Köker Özer is that realizing this promise requires evaluation frameworks aligned with clinical reality, not just with classification benchmarks. The reply, notably, was prepared with the assistance of ChatGPT for language refinement and editorial organization, with the authors critically reviewing the output and taking full responsibility for the content, an example of the transparency about AI use that journals increasingly expect.

The exchange also illustrates the healthy function of the Matters Arising format itself. Rather than letting an optimistic headline metric stand unchallenged, the correspondence process allows the community to interrogate what a result actually demonstrates and what it does not. Sanmoto and colleagues’ model represents a genuine step toward computer-aided diagnosis of neonatal pneumothorax, and the reply does not assert that the model fails; it insists that the evidence presented does not yet answer the question of whether the pneumothoraces that matter most are being caught. That distinction between not proven and disproven is exactly the kind of nuance that rigorous scientific debate is designed to surface, and it is a nuance that single-number summaries in press releases and abstracts tend to erase.

For clinicians, developers and regulators watching AI enter pediatric imaging, the lesson of this exchange is portable well beyond one model and one disease. Every deployment of diagnostic AI should begin by asking which errors are acceptable and which are catastrophic, and then demand performance data stratified accordingly. In neonatal pneumothorax, the catastrophic error is missing the large or tension leak in a newborn already struggling to breathe; the acceptable one may be a borderline call on a tiny asymptomatic collection that a clinician would watch anyway. Accuracy statistics that do not separate these scenarios are, as the Inonu University authors make clear, answering a question nobody in the intensive care unit is asking. The next generation of validation studies, evaluated against intervention thresholds and severity strata, will determine whether deep learning can truly earn a place beside the neonatal radiologist, and whether its confidence can be trusted when a newborn’s collapsed lung is quietly becoming an emergency.

Subject of Research: Evaluation of deep learning models for detecting clinically significant neonatal pneumothorax on chest radiographs

Article Title: Beyond overall accuracy: Does an artificial intelligence model detect the neonatal pneumothoraces that matter most? Reply to Sanmoto et al.

Article References: Tatar, D., Yılmaz, E., & Köker Özer, N. (2026). Beyond overall accuracy: Does an artificial intelligence model detect the neonatal pneumothoraces that matter most? Reply to Sanmoto et al.. Pediatric Radiology. https://doi.org/10.1007/s00247-026-06805-w

Image Credits: AI Generated

DOI: 10.1007/s00247-026-06805-w

Keywords: artificial intelligence, deep learning, neonatal pneumothorax, pediatric radiology, chest radiography, computer-aided diagnosis, neonatology, model validation, tension pneumothorax, clinical significance, chest drain, matters arising