Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis.

To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts. 

pdf/hotspots-and-trends-of-artificial-intelligence-in-the-field-4liksmnrkt.pdf

Hotspots and trends of artificial intelligence in the field of cataracts: a bibliometric analysis

Recent advances in high-speed network connections, cloud infrastructure, low-cost processing hardware, hardware miniaturization, and powerful learning algorithms like deep learning have revolutionized the healthcare system. These breakthroughs have led to the creation of point-of-care and wearable devices that can reach patients in their homes, enabled by miniaturization and flexible electronics. Moreover, inexpensive cloud connections facilitate the swift transmission of home test results to physicians, and abundant storage servers ensure an encrypted comprehensive anonymous history of the patient's treatment. AI algorithms offer predictive insights and decision-making support for practitioners, exemplified by the recent FDA approved AI software solution for diagnosing diabetic retinopathy in ophthalmology. This chapter explores various smart point-of-care AI algorithms being investigated for the early detection of ophthalmological diseases. The Theranocloud is a futuristic concept for a comprehensive healthcare solution that integrates Therapy, Diagnostics, and Cloud infrastructure to provide affordable and accessible therapy and diagnostic services for patients. 

Smart Sensor-Based Point-Of-Care Diagnostics in Ophthalmology: The Potential for Theranocloud as Combination of Theragnostic and Cloud Computing

Purpose of review The growing push to integrate telemedicine into ophthalmic practices requires physicians to have a thorough understanding of ophthalmic telemedicine's applications, limitations, and recent advances in order to provide well tolerated and appropriate clinical care. This review aims to provide an overview of recent advancements in the use of ophthalmic telemedicine for anterior segment eye examinations. Recent findings Virtual care for anterior segment evaluation relies on appropriate technology, novel workflows, and appropriate clinical case selection. Recent advances, particularly in the wake of the COVID-19 pandemic, have highlighted the utility of home-based assessments for visual acuity, external evaluation, tonometry, and refraction. Additionally, innovative workflows incorporating office-based testing into virtual care, termed ‘hybrid telemedicine’, enable high-quality ophthalmic testing to inform clinical decision-making. Summary Novel digital tools and workflows enable high-quality anterior segment evaluation and management for select ophthalmic concerns. This review highlights the clinical tools and workflows necessary to enable anterior segment telehealth. 

Implementation of anterior segment ophthalmic telemedicine

More than half of the cases of blindness in this industrialized world has their main cause as cataract. Loss of blindness and minimizing the suffering can be done by early diagnosis. However, due to the associated costs, clinical cataract detection and grading necessitate the expertise of qualified eye doctors, which may make everyone's early intervention difficult. The preset set of image features used in current analysis on automatic cataract detection and grading based on fundus pictures may give a redundant or even noisy conclusions. The effectiveness and efficiency of employing a deep convolutional neural network (DCNN) to automatically detect and grade cataract is investigated in this study. It also illustrates some of the feature maps at the pool 5 layer with their high-order empirical semantic meaning, explaining the feature representation extracted by the DCNN. Up to 5620 clinical retinal fundus images from hospitals, obtained as part of a population-based study, were used to cross-validate the proposed DCNN classification system. Two findings are proposed in this paper: First, interference from regionally inconsistent lighting and eye reflection was eliminated through the use of retinal fundus images after G-filter, which considerably enhances DCNN classification. 

A Real-Time Cataract Detection and Diagnosis Through Web-Based Imaging Analysis

Background: Cataract is the leading cause of blindness worldwide. In order to achieve large-scale cataract screening and remarkable performance, several studies have applied artificial intelligence (AI) to cataract detection based on fundus images. However, the fundus images they used are original from normal optical circumstances, which is less impractical due to the existence of poor-quality fundus images for inappropriate optical conditions in actual scenarios. Furthermore, these poor-quality images are easily mistaken as cataracts because both show fuzzy imaging characteristics, which may decline the performance of cataract detection. Therefore, we aimed to develop and validate an antiinterference AI model for rapid and efficient diagnosis based on fundus images. Materials and Methods: The datasets (including both cataract and noncataract labels) were derived from the Chinese PLA general hospital. The antiinterference AI model consisted of two AI submodules, a quality recognition model for cataract labeling and a convolutional neural networks-based model for cataract classification. The quality recognition model was performed to distinguish poor-quality images from normal-quality images and further generate the pseudo labels related to image quality for noncataract. Through this, the original binary-class label (cataract and noncataract) was adjusted to three categories (cataract, noncataract with normal-quality images, and noncataract with poor-quality images), which could be used to guide the model to distinguish cataract from suspected cataract fundus images. In the cataract classification stage, the convolutional-neural-network-based model was proposed to classify cataracts based on the label of the previous stage. The performance of the model was internally validated and externally tested in real-world settings, and the evaluation indicators included area under the receiver operating curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Results: In the internal and external validation, the antiinterference AI model showed robust performance in cataract diagnosis (three classifications with AUCs >91%, ACCs >84%, SENs >71%, and SPEs >89%). Compared with the model that was trained on the binary-class label, the antiinterference cataract model improved its performance by 10%. Conclusion: We proposed an efficient antiinterference AI model for cataract diagnosis, which could achieve accurate cataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.

Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study

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Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study