Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

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 Idi...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of retina and vitreous Ročník 10; číslo 1; s. 9 - 7
Hlavní autori: Valentim, Carolina C. S., Wu, Anna K., Yu, Sophia, Manivannan, Niranchana, Zhang, Qinqin, Cao, Jessica, Song, Weilin, Wang, Victoria, Kang, Hannah, Kalur, Aneesha, Iyer, Amogh I., Conti, Thais, Singh, Rishi P., Talcott, Katherine E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 23.01.2024
Springer Nature B.V
BMC
Predmet:
Algorithms
Annotations
Artificial Intelligence
Automation
Classification
Datasets
Deep learning
Diabetes
Macular degeneration
Macular hole
Medicine
Medicine & Public Health
Ophthalmology
Optical coherence tomography
Original
Original Article
Retina
Tomography
Deep learning
Macular hole
Optical coherence tomography
Artificial Intelligence
ISSN:2056-9920, 2056-9920
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2056-9920
2056-9920
DOI:10.1186/s40942-024-00526-8