{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T21:02:34Z","timestamp":1789419754228,"version":"build-2803163510"},"reference-count":35,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n The availability of dedicated image analysis scripts for adaptive optics (AO)\u2013flood illumination ophthalmoscopy (FIO) is limited, especially for large-scale measurements and analyses of nonhealthy images. This limitation highlights the need for alternative approaches to facilitate automated and scalable analysis. Large language models may help develop such scripts.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to generate an analysis script for AO-FIO images in the R programming language using a widely available generative AI (GenAI; specifically, GPT-4) as a proof of principle for generating a functional but nonvalidated script.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n GPT-4 was used to generate an R script for the analysis of AO-FIO images. The code generated by GPT-4 was fine-tuned iteratively based on trial and error, testing the script for image preprocessing and analysis using images from 4 participants, including 1 healthy individual and 3 patients with Stargardt disease. The script code was subsequently checked for errors by another researcher who was naive to previous coding, using a different test set of AO-FIO images from 4 other participants (n=1 healthy individual and n=3 patients with Stargardt disease). The cone counts from 5 AO image snippets were compared with the counts independently recorded by 2 human graders and those generated by pre-existing AO analysis software that was trained on healthy participants.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n After 54 iterations of instructions, a functional R script for the analysis of AO-FIO images was generated. The script identified and quantified blobs.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We developed a preliminary nonvalidated script for cone detection in retinal AO images with support from GPT-4. Before such a script can be used in clinical research, it should undergo more fine-tuning and extensive testing. Future work should enhance the image analysis capabilities of the script and validate its results to assess the potential of AO-based cone counts as biomarkers in clinical trials.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/94906","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T20:05:06Z","timestamp":1789416306000},"page":"e94906","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Optics Image Analysis Using Generative AI (GPT-4) Scripting: Exploratory Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0302-6690","authenticated-orcid":false,"given":"Jeroen AAH","family":"Pas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1694-0473","authenticated-orcid":false,"given":"Ludo C","family":"van der 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