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Valentim CCS, Wu AK, Yu S, Manivannan N, Zhang Q, Cao J, Song W, Wang V, Kang H, Kalur A, Iyer AI, Conti T, Singh RP, Talcott KE. Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography. Int J Retina Vitreous 2024; 10:9. [PMID: 38263402 PMCID: PMC10804727 DOI: 10.1186/s40942-024-00526-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Automated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans. METHODS In this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman's correlation was run to examine if the algorithm's probability score was associated with the severity stages of IFTMH. RESULTS Six hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman's correlation coefficient of 0.15 was achieved between the algorithm's probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied. CONCLUSIONS The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm's probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs.
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Affiliation(s)
- Carolina C S Valentim
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Anna K Wu
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sophia Yu
- Carl Zeiss Meditec, Inc, Dublin, CA, USA
| | | | | | - Jessica Cao
- Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Weilin Song
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Victoria Wang
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Hannah Kang
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Aneesha Kalur
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Amogh I Iyer
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Thais Conti
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Rishi P Singh
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Katherine E Talcott
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA.
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