Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning

Darlington Nwaudo; Jordan Serotte; Lewis Shi; Mansi Sakarvadia; Nicholas Maassen; Sahil Sethi; Sai Reddy

REVIEW 4 major objections 5 minor 30 references

Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a deep-learning ensemble can detect Bankart lesions on non-contrast shoulder MRIs with accuracy comparable to radiologists reading contrast-enhanced MR arthrograms, potentially reducing the need for invasive imaging.

desk verdict A legitimate first DL application to Bankart lesions with solid surgical ground truth, but the headline non-invasive parity claim rests on only 6 positive cases and needs reframing. read the letter →

arxiv 2412.06717 v1 pith:F6IBOSHH submitted 2024-12-09 cs.CV cs.AIcs.LG

Sahil Sethi , Sai Reddy , Mansi Sakarvadia , Jordan Serotte , Darlington Nwaudo , Nicholas Maassen , Lewis Shi This is my paper · ORCID

classification cs.CVcs.AIcs.LG

keywords BankartlesionglenoidlabralteardeeplearningstandardMRIMRarthrographySwinTransformermulti-viewensemblecomputer-aideddiagnosis

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that deep learning can diagnose Bankart lesions—tears of the anterior-inferior glenoid labrum—directly from standard, non-contrast shoulder MRIs, a setting where radiologists often miss the finding and patients are instead sent for invasive MR arthrography (MRA). Using arthroscopy as the gold standard, the authors trained separate Swin Transformer models on 335 standard MRIs and 251 MRAs, then ensembled sagittal, axial, and coronal predictions. The ensemble reached AUCs of 0.87 on standard MRIs and 0.90 on MRAs, with accuracy around 85 percent. The paper's headline finding is that on standard MRIs the model's 83.3 percent sensitivity far exceeded the 16.7 percent sensitivity of the original radiology reports, and its overall performance matched or surpassed radiologists interpreting MRAs. If true, this would give clinicians a non-invasive route to a diagnosis that today usually requires contrast injection or surgery.

What carries the argument

The load-bearing mechanism is a Swin Transformer, a vision-transformer architecture, pretrained on a public knee MRI dataset and fine-tuned separately on standard shoulder MRIs and MRAs. Each 3D MRI is treated as a series of 2D slices; slice-level features are aggregated by max pooling into a per-scan vector, and separate models for the sagittal, axial, and coronal views are trained and their output probabilities averaged. The decision threshold is set on the validation set at the point where sensitivity and specificity are equal, and ground-truth labels come from intraoperative arthroscopy findings, the gold standard for Bankart lesions.

What would settle it

Re-run the evaluation with radiologists explicitly asked to report on the anterior-inferior glenoid labrum for the same standard MRIs, blinded to arthroscopy results; if their focused-reading sensitivity matches or exceeds the model's 83.3 percent, the paper's central comparative claim fails. Alternatively, an external multi-site test set with more than six Bankart-positive standard MRIs could settle whether the 83.3 percent sensitivity (5 of 6) is real or a small-sample artifact.

Watch

Extended reading notes

Core claim

The central claim is that a multi-view deep-learning ensemble can detect Bankart lesions on both standard MRIs and MR arthrograms with diagnostic performance comparable to or better than radiologists, and specifically that the standard-MRI model rivals radiologist performance on MRAs. On a hold-out set of 71 standard MRIs (6 with Bankart lesions), the model achieved an AUC of 0.87, accuracy 85.9 percent, sensitivity 83.3 percent, and specificity 86.2 percent; on 46 MRAs (17 with lesions), it achieved an AUC of 0.90, accuracy 84.8 percent, sensitivity 82.4 percent, and specificity 86.2 percent. The paper reports that its standard-MRI sensitivity of 83.3 percent is far above the 16.7 percent sensitivity of the original radiology reports on the same scans and is within the 74–96 percent range reported in the literature for radiologists reading MRAs, which motivates the conclusion that non-invasive MRI plus deep learning could substitute for arthrography.

Load-bearing premise

The conclusion that the model surpasses radiologists rests on using routine clinical radiology reports as the radiologist baseline—reports not written for the specific task of detecting anterior-inferior labral tears—and on a test set with only six Bankart-positive standard MRIs, so both the comparison and the sensitivity estimate are fragile.

Editorial extensions

If this is right

A deep-learning screen on standard MRI could identify Bankart lesions without requiring contrast injection, avoiding the pain, cost, and rare complications of MR arthrography.
In the paper's test set, the standard-MRI model caught 5 of 6 Bankart lesions while the original radiology reports caught only 1 of 6; if replicated, this would address the main weakness of non-contrast shoulder MRI.
The multi-view ensemble outperformed every single-view model, so combining sagittal, axial, and coronal information appears important for detecting these subtle tears.
The MRA model's specificity of 86.2 percent sits slightly below the 91–98 percent range reported for radiologists, which the authors argue is clinically tolerable because imaging is interpreted alongside patient history.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If externally validated, the clinical pathway could shift toward standard MRI as the first-line imaging test for suspected Bankart lesions, reserving arthrography for cases where the model is uncertain or surgery is already planned.
The radiologist comparison is probably conservative in one way and optimistic in another: the reports were written for routine care rather than a focused anterior-inferior labrum assessment, so the 16.7 percent sensitivity may understate radiologists' true skill; conversely, the model was trained and tested on the same institution's equipment and protocols, so its edge could shrink on outside data
The test set contains only six Bankart-positive standard MRIs, so the reported 83.3 percent sensitivity corresponds to 5 correct detections; a reader should treat the sensitivity estimate as provisional until a larger, multi-site cohort is evaluated.
Because MRA and standard MRI cohorts differ in age, sex, and tear prevalence, the two models are not directly comparable; a model trained on one population may need recalibration before deployment in a different clinic.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a competent feasibility study that does one genuinely new thing—applying deep learning to Bankart lesions specifically, with intraoperative ground truth—but its headline claim is not supported by the number of positive cases behind it. The standard-MRI "matched or surpassed radiologists on MRA" conclusion rests on 5 true positives out of 6. One flipped prediction moves sensitivity by roughly 17 points; the 95% CI for 5/6 spans from about 36% to 99%. So the central comparison is statistically indistinguishable from chance at that sample size.

What the paper does well: surgical ground truth is the right reference standard, the two-surgeon labeling protocol with kappa=1.0 is a solid touch, and the multi-view Swin ensemble is a sensible, if standard, architecture. The MRA result (AUC 0.90 on 46 cases) is plausible and would be a useful data point on its own. The authors also deserve credit for being explicit in the conclusion that the standard-MRI cohort is highly imbalanced and that stability across splits needs future study; they don't hide the fragility.

Soft spots, in rough order of importance. First, the abstract and Section 4.2 overstate. "Matched or surpassed radiologists interpreting MRAs" is not established with 6 positives, regardless of how fair the radiologist baseline is. Second, the radiologist comparator is routine clinical reports, which were not written for a focused Bankart-reading task; that's a real validity threat for the specific comparison, though downstream of the sample-size issue. Third, the bootstrapped 95% CIs are shown as shaded bands but no numbers are given, which makes the CI claim hard to verify at reading time. Fourth, the dataset description is internally inconsistent: 546 patients in one place, 558 in another. Minor but should be fixed. I'd also note the threshold on standard MRI (0.71) was tuned on a validation set with about 4 positives; that's another source of optimism.

Net: the core MRA feasibility result is believable; the non-invasive parity claim is a hypothesis, not a result. This is the kind of paper a serious referee should see—it is a legitimate new application with real clinical motivation—but it needs revision: reframe the standard-MRI claim, report numeric CIs, and ideally add external validation.

I'd send it to peer review with a request for major revision. I would not cite the standard-MRI parity claim in my own work; I might cite the MRA model if the numbers hold up after revision.

Referee Report

4 major / 5 minor

Summary. The manuscript reports a deep learning pipeline for detecting Bankart lesions on both standard (non-contrast) shoulder MRI and MR arthrography (MRA). Separate Swin Transformer models are fine-tuned for each modality using arthroscopy-derived labels as ground truth, and multi-view ensembles of sagittal, axial, and coronal slice-level predictions are evaluated on a 20% hold-out test set (71 standard MRIs and 46 MRAs). The authors report AUCs of 0.87 and 0.90, with sensitivity/specificity around 83%/86% for standard MRI and 82%/86% for MRA. The central claim is that the standard-MRI model matches or surpasses radiologists interpreting MRAs, based on a comparison with retrospective clinical radiology reports. The paper is framed as a feasibility study and calls for external validation.

Significance. If the central claim were firmly established, the work would be clinically valuable because it could reduce reliance on invasive MRAs for a common shoulder pathology. The study has notable methodological strengths: ground truth comes from intraoperative arthroscopic findings, inter-rater agreement on a labeling subset is perfect (Fleiss's kappa = 1.0), the model is pre-trained on a relevant knee MRI dataset, and predictions are ensembled across multiple views. The MRA result (AUC 0.90 on 46 cases) is plausible and suggests the pipeline is functional. However, the headline comparison between standard MRI and radiologist-read MRA is not statistically supportable from the reported data, and the radiologist baseline is not derived from a controlled reading task. As presented, the significance of the paper is therefore limited to a promising proof-of-concept rather than a demonstrated clinical equivalence.

major comments (4)

[4.2, Table 2] The claim that the standard-MRI model 'matched or surpassed' radiologists interpreting MRAs rests on a sensitivity of 83.33% (5/6) in the standard-MRI test set. With only six positive cases, the 95% confidence interval for this proportion spans roughly 36% to 99%, and a single changed prediction moves sensitivity by about 17 percentage points. The difference between 5/6 and the radiologist MRA sensitivity of 14/17 is not statistically distinguishable. The manuscript must report confidence intervals for all sensitivity, specificity, and accuracy figures, and either temper the equivalence claim or present it with explicit acknowledgment of this instability.
[4.2, Table 2] The radiologist comparator is derived from original clinical radiology reports written for routine care, not from a focused evaluation of the anterior-inferior labrum. The reported standard-MRI sensitivity of 16.7% (1/6) is far below the 52-55% cited from the literature, which suggests the reports may not have targeted Bankart lesion detection. Without a reader study in which radiologists are asked to explicitly assess for Bankart lesions under the same conditions as the model, the 'surpasses radiologists' comparison is not a valid head-to-head evaluation. This issue affects the central claim and should be addressed by either performing a reader study or substantially reframing the conclusion.
[3.1, 4.2] The decision threshold for the standard-MRI ensemble (0.71) was selected on a validation set containing only about 4 positive cases (10% of 40). The resulting sensitivity/specificity pair is therefore fragile, and the reported operating point may not reflect the model's true performance at a clinically meaningful threshold. The authors should report the full ROC curve with numeric confidence intervals, and discuss how the operating point would change under alternative threshold-selection strategies or a larger validation set.
[4.1, Figure 4] The ROC curves in Figure 4 include shaded 95% confidence intervals calculated by bootstrapping, but no numeric interval values are given anywhere in the text or tables. Since the test set for standard MRI has only 6 positive cases, the AUC of 0.87 is likely accompanied by a very wide interval. The manuscript should report the numeric bootstrap intervals for both AUCs and for the threshold-dependent metrics in Table 2.

minor comments (5)

[2.1, 2.2, Table 1] There is an inconsistency in the patient count: Section 2.1 states 546 patients with 586 MRIs, while Section 2.2 and Table 1 report 558 patients. The abstract also says 558 patients. This should be reconciled.
[2.1] The inter-rater reliability statement reports a Fleiss's kappa of 1.0 on a 20-MRI subset. This is unusually perfect and may merit a brief explanation of the labeling procedure or the amount of discussion among raters.
[4.2] The phrase 'significantly exceeding radiologist sensitivity' is used without a statistical test. Given the small sample, it would be more accurate to say 'higher' or 'nominally higher' unless a formal comparison is provided.
[Table 1] The first column header 'Total MRIs1' appears to have a superscript issue, and the meaning of the superscript is not explained. Consider simplifying the header.
[2.3, 3.2] The augmentation procedure is described as 'ten-fold augmentation of training samples,' but it is not clear whether this increases the effective number of training epochs or the dataset size. A brief clarification would help reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: labels come from independent arthroscopic ground truth and hold-out evaluation is standard supervised practice.

full rationale

The paper's derivation chain is self-contained with respect to its target prediction. Ground-truth labels are intraoperative arthroscopic findings (Section 2.1, Fig. 2), independent of both the imaging inputs and the model outputs, so the supervised training and hold-out evaluation are not defined in terms of the quantities they are said to predict. The only tuned operating parameter is the decision threshold, chosen on the validation set at equal sensitivity and specificity (Section 3.1), before evaluation on the 20% hold-out test set; this is standard practice and does not force the reported test-set sensitivity or AUC. No load-bearing step relies on a self-citation or on an imported uniqueness theorem: the pretrained Swin encoder uses public MRNet and ImageNet weights, and the comparator metrics come from the paper's own hold-out predictions plus original radiology reports, which are external data rather than fitted outputs. The conclusion that standard-MRI performance 'surpasses radiologist' sensitivity depends on a small number of positive standard-MRI test cases (5/6) and on the assumption that original radiology reports are a fair focused-reading baseline; those are statistical and validity limitations, but they are not circular reductions in which a fitted parameter is renamed as a prediction or an equation reduces to its own input. Score 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

No new entities are introduced; the model is a standard architecture trained on clinical images. The main extra assumptions are domain-level: surgical labels, crop preservation, and transfer learning.

free parameters (2)

Decision threshold for standard MRI ensemble = 0.71
Chosen on the validation set as the point where sensitivity and specificity are equal; applied to the 71-scan test set.
Decision threshold for MRA ensemble = 0.19
Chosen on the validation set as the equal-sensitivity/specificity point; applied to the 46-scan MRA test set.

assumptions (3)

domain assumption Intraoperative findings are a correct gold standard for Bankart lesion status.
Ground truth labels were derived from intraoperative photos and operative notes; if the surgical documentation missed or mislabeled subtle tears, the reported metrics are biased. This is assumed throughout Sections 2.1 and 2.2.
domain assumption Resizing to n x 400 x 400 and center-cropping to 224 x 224 preserves the anterior-inferior labrum in all sequences.
Preprocessing in Section 2.3 discards image content outside the center crop; if the lesion-bearing region is cropped out, the model cannot learn the target feature.
domain assumption MRNet knee MRI pretraining transfers to shoulder MRI.
The model was initialized with weights pretrained on a knee MRI dataset (Section 3.1); the paper does not test whether pretraining helps or hurts on shoulder data.

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Cite this review

Pith. "Pith review of Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning." pith.science (2026). https://pith.science/paper/F6IBOSHH

@misc{pith2026241206717,
  author       = {Pith},
  title        = {Pith review of: Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F6IBOSHH}},
  note         = {Machine review of arXiv:2412.06717}
}

read the original abstract

Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for Bankart lesion diagnosis. Separate DL models for MRAs and standard MRIs were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). Bankart lesions were identified in 31.9% of MRAs and 8.6% of standard MRIs. The models achieved AUCs of 0.87 (86% accuracy, 83% sensitivity, 86% specificity) and 0.90 (85% accuracy, 82% sensitivity, 86% specificity) on standard MRIs and MRAs, respectively. These results match or surpass radiologist performance on our dataset and reported literature metrics. Notably, our model's performance on non-invasive standard MRIs matched or surpassed the radiologists interpreting MRAs. This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care.

Figures

Figures reproduced from arXiv: 2412.06717 by the authors.

**Figure 2.** Data Collection and Labeling Protocol. including pneumonia detection and diabetic retinopathy grading,24, 25 its application in orthopedic imaging remains underexplored. By leveraging an ensemble approach across sagittal, axial, and coronal views, our models achieve comparable sensitivity and specificity on standard MRIs compared to radiologists on invasive MRAs, demonstrating the potential of DL to address this dia… view at source ↗

**Figure 3.** Model Training & Inference (a) Schematic of 2D model training setup using 3D MRIs. (b) Schematic [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Receiver operating characteristic (ROC) curves for single-view models and the multi-view ensemble, [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

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