Introduction

Gastric vascular ectasias are a common complication of liver cirrhosis and resultant portal hypertension. Traditional therapy with argon plasma coagulation is expensive. Endoscopic band ligation offers a simpler and more cost effective therapy with similar results.

Aims & Methods

Comparison of efficacy of endoscopic band ligation (EBL) with argon plasma coagulation (APC) in the management of bleeding gastric vascular ectasias (GVE).
The study period was from August 2021 to April 2025. It was carried out in the Department of Gastroenterology and Hepatology, Shaikh Zayed Hospital, Lahore, Pakistan. It was a randomized controlled trial.
30 patients of chronic liver disease who had presented with upper gastrointestinal bleeding due to Gastric Vascular Ectasias , as seen on upper GI endoscopy were recruited. All patients were explained procedural details and informed consent was taken. They were then randomly divided into two groups, A & B, of 15 patients each. Patient in group A underwent EBL while those in group B were treated with APC. After initial endoscopic therapy, these patients were followed up at 2 and 4 weeks. Efficacy of therapy was assessed by using clinical parameters (recurrence of bleeding manifest as hematemesis/malaena/both), laboratory parameters (drop in hemoglobin) and endoscopic parameters ( GVE bleed seen after initial therapy).

Results

One patient from both groups was lost to follow up. Four patients, two out of each group passed away before their last endoscopy session.
In group A (EBL), 4 out of the remaining 12 patients (33 %) needed repeat therapy at 4 weeks while 8 patients (66%) showed post banding healing not requiring repeat banding during the study period.
In group B (APC), 5 out of remaining 12 patients (42%) needed repeat therapy at 4 weeks. 7 out of 12 patients (58 %) did not need repeat therapy. There was one episode of GI bleeding in a patient undergoing band ligation which was managed conservatively with PPI and surcralfate. No other significant procedure related side effects were encountered in either groups.

Conclusion

Endoscopic Band Ligation is comparable in efficacy to Argon Plasma Coagulation for management of Gastric Vascular ectasias, offering significant cost benefits and ease of therapy.

References

1-Keohane J, Berro W, Harewood GC, Murray FE, Patchett SE. Band ligation of gastric antral vascular ectasia is a safe and effective endoscopic treatment. Dig Endosc. 2013 Jul;25(4):392-6.
2- Mohan BP, Toy G, Kassab LL, Ponnada S, Chandan S, Parbhu S, Chandna S, Adler DG. Endoscopic band ligation in the treatment of gastric antral vascular ectasia: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Dec;94(6):1021-1029.e10. doi: 10.1016/j.gie.2021.08.017. Epub 2021 Sep 1. PMID: 34480922.

Disclosure

There were no conflicts of interest in the course of this study

Introduction

Artificial intelligence (AI) characterization (CADx) systems for colonic polyps exhibit high diagnostic accuracy. However, they may only support, not replace, clinical decision-making and little is known of this human-AI interaction – even less so with trainee endoscopists. This issue is difficult to explore, but data from a study investigating CADx-supported trainee endoscopists compared to AI-blinded experts could give insight.

Aims & Methods

This analysis draws on data from a prospective observational study comparing the diagnostic performance of CADx-supported trainee endoscopists (using GI Genius®, Medtronic) to expert endoscopists blinded to AI support. Two key scenarios were explored: (a) instances in which the CADx system produced a ‘no prediction’ output, and (b) cases where trainees actively overruled AI-generated diagnoses. These were assessed to examine the impact of AI absence and human-AI discordance on diagnostic accuracy.

Results

The baseline cohort included 11 trainees performing 225 colonoscopies, mean age 63.8 years (SD 12.7), 48.9% male patients. Baseline ADR was 57.8%. Of 630 resected lesions (median size 3 mm, IQR 2;4), 40.0% were in the rectosigmoid, and 53.6% were non-adenomatous. CADx failed to provide a prediction in 19.2% (n=121) of cases. These ‘no prediction’ polyps were smaller (median 3 mm, p=0.048) and more often located in the right colon (72% vs. 58%, p=0.005. In the absence of AI support, trainee diagnostic accuracy declined significantly by 12.8% (63.6% vs. 76.4%, p=0.007). Interestingly, expert endoscopists—despite being blinded to AI—also performed worse on these polyps (64.7% vs. 73.9%, p=0.045), suggesting an inherent difficulty in classifying these lesions.
Trainees overruled AI suggestion leading to discordant diagnoses in 9.0% (57/630) of polyps, comprised of 20 (35.1%) non-adenomatous, and 37 (64.9%) adenomatous lesions. Trainees incorrectly overruled AI in 59.6% (34/57), which mostly consisted of downgrading lesions to NICE 1. Discordant polyps did not differ in size (3 mm vs. 3 mm, p=0.413), but were more often located proximally (77.2% vs. 58.1%, p=0.005). The difference in false negative rate (FNR) between trainees and AI was 62.16% (81.07% vs. 18.92%, p<0.0001), demonstrating a risk of missed adenomas when trainees override AI. Blinded expert endoscopists achieved identical overall diagnostic accuracy in those polyps as the AI system (59.6%). However, their error profiles differed – In this subset, blinded experts and AI performed equally in overall accuracy (59.6%), although with differing error profiles: AI showed a numerically lower FNR (18.9% vs. 35.1%, Δ16.2%, p=0.17), offset by a higher false positive rate (80.0% vs. 50.0%).

Conclusion

CADx systems generate ‘no prediction’ outputs in a substantial proportion of polyps, significantly impairing trainee diagnostic performance. These cases may represent inherently more challenging lesions. Despite CADx’s high accuracy, trainees overrule AI in almost every tenth polyp, most often incorrectly downstaging polyps. Structured training on appropriate use of CADx, emphasizing both its capabilities and limitations, may enhance clinical effectiveness. Further research on human-AI interaction and trust is warranted, as these factors will be critical for successful integration of CADx into routine endoscopic practice.

Disclosure

The original study (AC-CADx) received technical support by Medtronic. Medtronic was not involved in the design of the study or analysis of the data, neither in the original study nor this exploratory analysis.

Introduction

Advances in AI have improved lesion detection in upper gastrointestinal (GI) endoscopy. However, most systems still behave as black boxes, providing indings without explanation. In a clinical setting, this lack of interpretability limits adoption. In contrast, vision-language models (VLMs) can provide explanatory outputs, but they lack performance and efficiency. We propose a hybrid system that combines a high-performance lesion detector with a compact VLM to produce structured explanations for upper GI endoscopic images, improving both accuracy and transparency. This system aims to improve classification accuracy for neoplastic vs. non-neoplastic lesions and enhance clinical explainability while enabling effective inference with realistically limited datasets, maintaining clinically practical inference speed during procedures. During the endoscopic procedure, CNN-based detectors can perform at around 10ms per input. Down-stream small VLM can generate 92.8 tokens per second, which is suitable for post-procedure evaluation.

Aims & Methods

We developed a multimodal pipeline integrating a CNN-based lesion detector with a lightweight VLM (Gemma-3-4B). Given an input endoscopic image, the DETR-RT detector predicts lesion type (p0/p1) and bounding boxes, which are passed as metadata inputs to a SigLIP image encoder and a Gemma-3 small language model. Finally, our pipeline outputs description of the context including organ identification, lesion classification, and explanation of the detector’s prediction. We employed a distillation process, in which explanations and descriptions of the input endoscopic image are generated by a very large model such as Gemini-2.5-Pro and GPT-4.1. These descriptions are then used to train a small language model based on Gemma-3 with 4 billion parameters. We prepared 1560 images from 246 clinical cases as experimental data. 1247 images (197 cases) are used for training and the remaining 313 images (49 cases) are used for testing.

Results

On a held-out test set of gastric cases, the system model achieved a 67% sensitivity and 85% specificity of distinguishing neoplastic from non-neoplastic lesions —representing 3% and 2% improvement respectively over the detection model (DETR-RT) alone. Across the held-out test set, structured explanations included consistent fields (organ, lesion type, morphology, rationale) with 100% completeness. Component-wise accuracy: organ (85%), light color (100%), lesion type (75%), and JGES classification (83%).

Conclusion

We developed a compact VLM that generates structured, clinically meaningful explanations for endoscopy images. By fine-tuning with synthetic data and integration of a detector, the model not only identifies abnormalities but explains them based on key morphological and contextual features. Despite limited train data, it achieved high accuracy and interpretability, outperforming detection-only baselines.This approach adds structured explanations without sacrificing performance, enabling efficient training and improving both diagnostic accuracy and transparency. By encoding established endoscopic diagnostic criteria into the output, the system presents clinicians with synthesized clinical knowledge—supporting informed interpretation and enhancing trust in AI-assisted decision making. Notably, the explanations for false positives often referenced benign patterns (e.g., folds, shadows), which are anticipated to support clinicians in interpreting uncertain outputs and assessing potential misjudgments.

References

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