Deep Learning for Prediction of AMD Progression: A Pilot Study

Purpose: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning. Methods: Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n = 40) at year 2 and those that had (n = 31). Two deep convolutional neural networks (CNN) were evaluated using 5-fold cross validation on the OCT data at baseline to attempt to predict which eyes would convert to advanced AMD at year 2: (1) VGG16, a popular CNN for image recognition was fine-tuned, and (2) a novel, simplified CNN architecture was trained from scratch. Preprocessing was added in the form of a segmentation-based normalization to reduce variance in the data and improve performance. Results: Our new architecture, AMDnet, with preprocessing, achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 at the B-scan level and 0.91 for volumes. Results for VGG16, an established CNN architecture, with preprocessing were 0.82 for B-scans/0.87 for volumes versus 0.66 for B-scans/0.69 for volumes without preprocessing. Conclusions: A CNN with layer segmentation-based preprocessing shows strong predictive power for the progression of early/intermediate AMD to advanced AMD. Use of the preprocessing was shown to improve performance regardless of the network architecture

Instrument Pose Estimation Using Registration for Otobasis Surgery

Clinical outcome of several Minimally Invasive Surgeries (MIS) heavily depend on the accuracy of intraoperative pose estimation of the surgical instrument from intraoperative x-rays. The estimation consists of finding the tool in a given set of x-rays and extracting the necessary data to recreate the tool’s pose for further navigation - resulting in severe consequences of incorrect estimation. Though state-of-the-art MIS literature has exploited image registration as a tool for instrument pose estimation, lack of practical considerations in previous study design render their conclusion ineffective from a clinical standpoint. One major issue of such a study is the lack of Ground Truth in clinical data -as there are no direct ways of measuring the ground truth pose and indirect estimation accumulates error. A systematic way to overcome this problem is to generate Digitally Reconstructed Radiographs (DRR), however, such procedure generates data which are free from measuring errors (e.g. noise, number of projections), resulting claims of registration performance inconclusive. Generalization of registration performance across different instruments with different Degrees of Freedom (DoF) has not been studied as well. By marrying a rigorous study design involving several clinical scenarios with, for example, several optimizers, metrics and others parameters for image registration, this paper bridges this gap effectively. Although the pose estimation error scales inversely with instrument size, we show image registration generalizes well for different instruments and DoF. In particular, it is shown that increasing the number of x-ray projections can reduce the pose estimation error significantly across instruments - which might lead to the acquisition of several x-rays for pose estimation in a clinical workflow

Predicting conversion to wet age-related macular degeneration using deep learning

Progression to exudative ‘wet’ age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression