Abstract
Cataract constitutes half of the blindness cases worldwide; hence, detecting and treating cataracts in a timely manner are effective strategies for blindness prevention. Recently, methods of detecting cataracts through deep learning are flourishing; however, the task of improving the grading mechanism is still the priority in the research field. This study evaluates the classification capability of the automated nuclear cataract detection algorithm using ocular images captured by smartphone-based slit-lamp. The task of the algorithm is to automatically detect cataract severity in terms of the photometric appearance of the nuclear region of the crystalline lens of the eyes. The nuclear region of the ocular lens was localized by YOLOv3. Subsequently, the combination of a deep learning network, ShuffleNet, and a support vector machine (SVM) classifier was used to grade cataract severity, evaluating the gray conjugate features of the nuclear region. Using the trained algorithm, 819 anterior ocular images captured by smartphone-based slit-lamp were utilized to evaluate the algorithm's performance. The accuracy was 93.5% with Kappa of 95.4% and F1 of 92.3%. The AUC was 0.9198. The proposed validation method could evaluate a cataract severity in 29 ms and the entire classification process in less than 1s. This study can improve the accuracy of the examination, reduce misdiagnosis rate and the difficulty of the doctor's examination. The addition of scoring system can improve the quality of pictures obtained by non-ophthalmologists. The method is especially suitable for cataract screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources. It can also improve the accessibility of ophthalmic medical treatment.
Highlights
Today, the world has approximately 400 million vision impairment and 40 million blind population [1]
The method is especially suitable for cataract screening in the underdeveloped areas or areas which are in shortage of ophthalmic resources
We mainly evaluate our models on the Marked Slit Lamp Picture Project (MSLPP) classification dataset
Summary
The world has approximately 400 million vision impairment and 40 million blind population [1]. Posterior cystic cataract is a lesion in the posterior capsule of the lens If it is located in the visual axis region, it can affect vision in the early stage. Automatic grading will improve diagnostic efficiency and avoid the involvement of subjective factors It can effectively improve the clinical management of cataract disease, and provide theoretical basis for the epidemiology [9]. Our primary contribution is to propose an innovative framework for automated classification using artificial intelligence This framework integrates three different artificial intelligence networks by means of logical regression, which improves the accuracy of diagnosis and reduces the false positive rate in the process of screening, so that doctors can obtain the crystal picture of the patient.
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