{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:19:41Z","timestamp":1787318381702,"version":"build-2736575974"},"reference-count":72,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007414","name":"Deanship of Graduate Studies and Scientific Research at Qassim University","doi-asserted-by":"publisher","award":["QU-APC-2026"],"award-info":[{"award-number":["QU-APC-2026"]}],"id":[{"id":"10.13039\/501100007414","id-type":"DOI","asserted-by":"publisher"}]},{"award":["QU-APC-2026"],"award-info":[{"award-number":["QU-APC-2026"]}],"id":[{"id":"https:\/\/ror.org\/01wsfe280","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Intelligent decision support systems for colonoscopy can fail when encountering out-of-distribution surgical instruments such as snares or biopsy forceps, producing false-positive polyp masks that may contribute to automation bias and reduce workflow reliability. This study aimed to develop and evaluate a parameter-efficient framework for open-set-aware polyp segmentation that can reject such anomalous inputs while preserving in-distribution segmentation performance. Methods: We propose PolypSAM-Open, which integrates a prototype-based Open-Set Learning (OSL) module with Low-Rank Adaptation (LoRA) in the MedSAM image encoder. The model was trained on Kvasir-SEG using an 85\/15 split of authentic polyp images and synthetic high-frequency Gaussian noise to learn a rejection margin. Zero-shot out-of-distribution detection was evaluated on 590 unseen authentic surgical instruments from Kvasir-Instrument. Segmentation and detection performance were compared against a standard MedSAM-LoRA baseline. Results: Standard parameter-efficient fine-tuning yielded an OOD AUROC of 0.4263 on authentic surgical instruments. PolypSAM-Open improved zero-shot OOD AUROC to 0.9535 (p&lt;0.001). Despite allocating 15% of training capacity to the synthetic-noise rejection margin, PolypSAM-Open maintained segmentation performance comparable to the standard fine-tuned baseline (Dice 0.9728 versus 0.9723). On ETIS-LaribPolypDB and CVC-ClinicDB, Dice scores were 0.9301 and 0.9386, respectively. The approach remained parameter-efficient, updating 4.48% of total parameters while adding negligible inference latency relative to the underlying MedSAM forward. Conclusions: Prototype-based open-set adaptation can substantially improve rejection of unseen surgical artifacts in AI-assisted colonoscopy while preserving high segmentation accuracy. These findings position PolypSAM-Open as a promising strategy for potentially safer decision-support segmentation in endoscopic workflows; prospective clinical validation remains necessary.<\/jats:p>","DOI":"10.3390\/diagnostics16142226","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:55:59Z","timestamp":1784249759000},"page":"2226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PolypSAM-Open: Mitigating Automation Bias in AI-Assisted Colonoscopy via Open-Set Surgical Artifact Rejection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6659-2006","authenticated-orcid":false,"given":"Umar","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7408-4540","authenticated-orcid":false,"given":"Shadman","family":"Shahriar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0135-8794","authenticated-orcid":false,"given":"Faiyad","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4031-6996","authenticated-orcid":false,"given":"Md Alamgir","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3420-3597","authenticated-orcid":false,"given":"Muhammad Ali","family":"Martuza","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8683-7247","authenticated-orcid":false,"given":"Sifat","family":"Momen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, School of Engineering and Physical Sciences, North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/TMI.2017.2664042","article-title":"Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge","volume":"36","author":"Bernal","year":"2017","journal-title":"IEEE Trans. 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