eprintid: 10193125 rev_number: 10 eprint_status: archive userid: 699 dir: disk0/10/19/31/25 datestamp: 2024-06-07 08:35:36 lastmod: 2024-06-07 08:36:24 status_changed: 2024-06-07 08:35:36 type: article metadata_visibility: show sword_depositor: 699 creators_name: Antaki, Fares creators_name: Chopra, Reena creators_name: Keane, Pearse A title: Vision-Language Models for Feature Detection of Macular Diseases on Optical Coherence Tomography ispublished: inpress divisions: UCL divisions: B02 divisions: C07 divisions: D08 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. abstract: IMPORTANCE: Vision-language models (VLMs) are a novel artificial intelligence technology capable of processing image and text inputs. While demonstrating strong generalist capabilities, their performance in ophthalmology has not been extensively studied. OBJECTIVE: To assess the performance of the Gemini Pro VLM in expert-level tasks for macular diseases from optical coherence tomography (OCT) scans. DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional diagnostic accuracy study evaluating a generalist VLM on ophthalmology-specific tasks using the open-source Optical Coherence Tomography Image Database. The dataset included OCT B-scans from 50 unique patients: healthy individuals and those with macular hole, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. Each OCT scan was labeled for 10 key pathological features, referral recommendations, and treatments. The images were captured using a Cirrus high definition OCT machine (Carl Zeiss Meditec) at Sankara Nethralaya Eye Hospital, Chennai, India, and the dataset was published in December 2018. Image acquisition dates were not specified. EXPOSURES: Gemini Pro, using a standard prompt to extract structured responses on December 15, 2023. MAIN OUTCOMES AND MEASURES: The primary outcome was model responses compared against expert labels, calculating F1 scores for each pathological feature. Secondary outcomes included accuracy in diagnosis, referral urgency, and treatment recommendation. The model's internal concordance was evaluated by measuring the alignment between referral and treatment recommendations, independent of diagnostic accuracy. RESULTS: The mean F1 score was 10.7% (95% CI, 2.4-19.2). Measurable F1 scores were obtained for macular hole (36.4%; 95% CI, 0-71.4), pigment epithelial detachment (26.1%; 95% CI, 0-46.2), subretinal hyperreflective material (24.0%; 95% CI, 0-45.2), and subretinal fluid (20.0%; 95% CI, 0-45.5). A correct diagnosis was achieved in 17 of 50 cases (34%; 95% CI, 22-48). Referral recommendations varied: 28 of 50 were correct (56%; 95% CI, 42-70), 10 of 50 were overcautious (20%; 95% CI, 10-32), and 12 of 50 were undercautious (24%; 95% CI, 12-36). Referral and treatment concordance were very high, with 48 of 50 (96%; 95 % CI, 90-100) and 48 of 49 (98%; 95% CI, 94-100) correct answers, respectively. CONCLUSIONS AND RELEVANCE: In this study, a generalist VLM demonstrated limited vision capabilities for feature detection and management of macular disease. However, it showed low self-contradiction, suggesting strong language capabilities. As VLMs continue to improve, validating their performance on large benchmarking datasets will help ascertain their potential in ophthalmology. date: 2024-05-02 date_type: published publisher: American Medical Association official_url: http://dx.doi.org/10.1001/jamaophthalmol.2024.1165 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2275042 doi: 10.1001/jamaophthalmol.2024.1165 medium: Print-Electronic pii: 2818270 lyricists_name: Antaki, Fares lyricists_name: Keane, Pearse lyricists_name: Chopra, Reena lyricists_id: FANTA67 lyricists_id: KPEAR28 lyricists_id: RCHOP17 actors_name: Keane, Pearse actors_id: KPEAR28 actors_role: owner full_text_status: public publication: JAMA Ophthalmology pages: 4 event_location: United States citation: Antaki, Fares; Chopra, Reena; Keane, Pearse A; (2024) Vision-Language Models for Feature Detection of Macular Diseases on Optical Coherence Tomography. JAMA Ophthalmology 10.1001/jamaophthalmol.2024.1165 <https://doi.org/10.1001/jamaophthalmol.2024.1165>. (In press). Green open access document_url: https://discovery-pp.ucl.ac.uk/id/eprint/10193125/3/Keane_Vision-Language%20Models%20for%20Feature%20Detection%20of%20Macular%20Diseases%20on%20Optical%20Coherence%20Tomography_AAM.pdf