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Substantial progress has been made through landmark studies like that of the Progression of Atrophy Secondary to Stargardt Disease (ProgStar), but tasks like image segmentation and phenotyping still pose major challenges in terms of monitoring disease progression and categorizing patient subgroups. Furthermore, these methods are subjective and laborious. Recent advancements in machine learning (ML) and deep learning show considerable promise in automating these processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This scoping review explores ML applications in ABCA4R, with a focus on segmentation and phenotyping. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, 15 articles were selected from 264, with 12 focused on the task of segmenting atrophic lesions, retinal flecks, retinal layer boundaries, or en-face imaging. Three studies addressed phenotyping based on electroretinography (ERG), visual acuity, and microperimetry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Several effective approaches were implemented in these studies, including ensemble modeling, self-attention mechanisms, soft-label approaches, and dynamic frameworks that consider extent of tissue damage. Excellent model performance includes segmentation DICE performances of 0.99 and ERG phenotyping accuracies 90% and greater. Smaller datasets and variable presentations present as significant challenges, while advanced methods like Monte Carlo dropout and active learning improve pipeline efficiency and performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML techniques are well on their way to automate key steps in ABCA4R evaluation with excellent performance. These emerging methods have the potential to expedite therapeutic innovation and enhance our understanding of ABCA4R.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10792-025-03690-4","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T06:13:35Z","timestamp":1753251215000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping"],"prefix":"10.1007","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-4693","authenticated-orcid":false,"given":"Yousif J.","family":"Shwetar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1916-7551","authenticated-orcid":false,"given":"Brian P.","family":"Brooks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9549-0644","authenticated-orcid":false,"given":"Brett G.","family":"Jeffrey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-1023","authenticated-orcid":false,"given":"Benjamin D.","family":"Solomon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9114-8737","authenticated-orcid":false,"given":"Melissa A.","family":"Haendel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"3690_CR1","first-page":"534","volume":"71","author":"K Stargardt","year":"1909","unstructured":"Stargardt K (1909) \u00dcber famili\u00e4re, progressive degeneration in der Maculagegend des Auges. 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