Artificial intelligence in predicting macular hole surgery outcomes: A focus on optical coherence tomography parameters

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dc.authorid0000-0002-8520-0073
dc.authorid0000-0001-7173-8617
dc.authorid0000-0002-7827-9887
dc.authorid0000-0002-9673-824X
dc.contributor.authorÖztürk, Yücel
dc.contributor.authorAğın, Abdullah
dc.contributor.authorYelmi, Burcu
dc.contributor.authorZorlutuna Kaymak, Nilufer
dc.date.accessioned2025-08-01T08:27:05Z
dc.date.available2025-08-01T08:27:05Z
dc.date.issued2025
dc.departmentFakülteler, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Göz Hastalıkları Ana Bilim Dalı
dc.description.abstractPurpose To evaluate the predictive performance of optical coherence tomography (OCT)-based indices and artificial intelligence (AI) using a Generative Pre-Trained Transformer (GPT) model and compare them with traditional logistic regression in forecasting anatomical success following macular hole (MH) surgery. Methods This retrospective observational study included 51 eyes of 51 patients who underwent pars plana vitrectomy for idiopathic MH. Preoperative OCT measurements of macular hole index (MHI), traction hole index (THI), hole form factor (HFF), basal hole diameter (BHD), and minimum hole diameter (MHD) were recorded. GPT-based AI predictions were generated using masked input data. A logistic regression model was developed with the same variables. Predictive performance was assessed using accuracy, area under the curve (AUC), positive predictive value (POPV), negative predictive value (NPV), and Kappa statistics. Results Anatomical success was achieved in 72.5% of cases. MHI, THI, and HFF were significantly higher in the successful group (p<0.0001). GPT achieved an accuracy of 77.0% and AUC of 0.770, with perfect POPV (1.000) but low NPV (0.452). Logistic regression outperformed GPT, achieving an accuracy of 84.3%, an AUC of 0.759, a higher NPV (0.800), and better agreement (Kappa 0.568 vs. 0.392). BHD and MHD showed poor predictive power (AUC 0.291). Conclusion OCT-derived indices, especially MHI, THI, and HFF, effectively predict MH surgery outcomes. Logistic regression based on actual patient data demonstrated superior predictive performance compared to GPT. AI models hold potential but require further development, integration of multimodal data, and validation before clinical application.
dc.identifier.citationÖztürk, Y., Ağın, A., Yelmi, B., & Zorlutuna Kaymak, N. (2025). Artificial intelligence in predicting macular hole surgery outcomes: A focus on optical coherence tomography parameters. BMC Ophthalmology, 25(1), pp. 1-8. https://doi.org/10.1186/s12886-025-04256-9
dc.identifier.doi10.1186/s12886-025-04256-9
dc.identifier.endpage8
dc.identifier.issn1471-2415
dc.identifier.issue1
dc.identifier.pmidPMID: 40696275
dc.identifier.scopus2-s2.0-105011250708
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1186/s12886-025-04256-9
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1057
dc.identifier.volume25
dc.identifier.wosWOS:001537787500002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expanded
dc.institutionauthorÖztürk, Yücel
dc.institutionauthorid0000-0002-8520-0073
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofBMC Ophthalmology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMacular Hole
dc.subjectArtificial Intelligence
dc.subjectGPT
dc.subjectTHI
dc.subjectMHI
dc.subjectHFF
dc.subjectBHD
dc.titleArtificial intelligence in predicting macular hole surgery outcomes: A focus on optical coherence tomography parameters
dc.typeArticle
dspace.entity.typePublication

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