Reshaping the use of Artificial Intelligence in Ophthalmology: Sometimes you Need to go Backwards

Background: Around 30% of non-exudative macular neovascularizations(NE-MNVs) exudate within 2 years from diagnosis in patients with age-related macular degeneration(AMD).The aim of the study is to develop a deep learning classifier based on optical coherence tomography(OCT) and OCT angiography(OCTA) to identify NE-MNVs at risk of exudation. Methods: AMD patients showing OCTA and fluorescein angiography (FA) documented NE-MNV with a 2-years minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented MNV exudation within the first 2 years formed the EX-GROUP while the others formed QU-GROUP.ResNet-101, Inception-ResNet-v2 and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the 6 models were evaluated with major and soft voting techniques. Results: Eighty-nine (89) eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited(35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the 3 single convolutional neural networks(CNNs).The major voting model resulting from the association of the 3 different CNNs resulted in improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders’ performance). Soft voting model resulting from the combination of OCTA and OCT B-scan based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA enface imaging were more prevalent in QU GROUP. Conclusions: Artificial intelligence shows high performances in identifications of NE-MNVs at risk for exudation within the first 2 years of follow up, allowing better customization of follow up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.

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Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra‐wide field imaging

Catania,

Chapron,

Crincoli

et al. 2024

Acta Ophthalmologica

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PurposeTo elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra‐wide field (UWF) images obtained preoperatively and postoperatively.Materials and MethodsWe retrospectively included patients >18 years who underwent either scleral buckling (SB) or pars plana vitrectomy (PPV) for primary or recurrent RRD with a post‐operative follow‐up >2 years. Records of RRD recurrence between 6 weeks and 2 years after surgery served as a ground truth for the training of the deep learning (DL) models. Four separate DL models were trained to predict LR within the 2 postoperative years (binary outputs) using, respectively, UWF preoperative and postoperative pseudocolor images and UWF preoperative and postoperative AF images.ResultsA total of 412 eyes were included in the study (332 eyes treated with PPV and 80 eyes with SB). The mean follow‐up was 4.0 ± 2.1 years. The DL models based on preoperative and postoperative pseudocolor UWF imaging predicted recurrence with 85.6% (sensitivity 86.7%, specificity 85.4%) and 90.2% accuracy (sensitivity 87.0%, specificity 90.8%) in PPV‐treated eyes, and 87.0% (sensitivity 86.7%, specificity 87.0%) and 91.1% (sensitivity 88.2%, specificity 91.9%) in SB‐treated eyes, respectively. The DL models using preoperative and postoperative AF‐UWF imaging predicted recurrence with 87.6% (sensitivity 84.0% and specificity 88.3%) and 91.0% (sensitivity 88.9%, specificity 91.5%) accuracy in PPV eyes, and 86.5% (sensitivity 87.5%; specificity 86.2%) and 90.6% (sensitivity 90.0%, specificity 90.7%) in SB eyes, respectively. Among the risk factors detected with visualisation methods, potential novel ones were extensive laser retinopexy and asymmetric staphyloma.ConclusionsDL can accurately predict the LR of RRD based on UWF images (especially postoperative ones), which can help refine follow‐up strategies. Saliency maps might provide further insight into the dynamics of RRD recurrence.

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Reshaping the use of Artificial Intelligence in Ophthalmology: Sometimes you Need to go Backwards

Cited by 4 publications

References 16 publications

Prediction of Functional and Anatomic Progression in Lamellar Macular Holes

Prediction of Functional and Anatomic Progression in Lamellar Macular Holes

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD

Deep Learning for prediction of late recurrence of retinal detachment using preoperative and postoperative ultra‐wide field imaging

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