Early detection of visual impairment in young children using a smartphone-based deep learning system

Evaluating the effectiveness of large language models in patient education for conjunctivitis

AIMS

To evaluate the quality of responses from large language models (LLMs) to patient-generated conjunctivitis questions.

METHODS

A two-phase, cross-sectional study was conducted at the Eye and ENT Hospital of Fudan University. In phase 1, four LLMs (GPT-4, Qwen, Baichuan 2 and PaLM 2) responded to 22 frequently asked conjunctivitis questions. Six expert ophthalmologists assessed these responses using a 5-point Likert scale for correctness, completeness, readability, helpfulness and safety, supplemented by objective readability analysis. Phase 2 involved 30 conjunctivitis patients who interacted with GPT-4 or Qwen, evaluating the LLM-generated responses based on satisfaction, humanisation, professionalism and the same dimensions except for correctness from phase 1. Three ophthalmologists assessed responses using phase 1 criteria, allowing for a comparative analysis between medical and patient evaluations, probing the study's practical significance.

RESULTS

In phase 1, GPT-4 excelled across all metrics, particularly in correctness (4.39±0.76), completeness (4.31±0.96) and readability (4.65±0.59) while Qwen showed similarly strong performance in helpfulness (4.37±0.93) and safety (4.25±1.03). Baichuan 2 and PaLM 2 were effective but trailed behind GPT-4 and Qwen. The objective readability analysis revealed GPT-4's responses as the most detailed, with PaLM 2's being the most succinct. Phase 2 demonstrated GPT-4 and Qwen's robust performance, with high satisfaction levels and consistent evaluations from both patients and professionals.

CONCLUSIONS

Our study showed LLMs effectively improve patient education in conjunctivitis. These models showed considerable promise in real-world patient interactions. Despite encouraging results, further refinement, particularly in personalisation and handling complex inquiries, is essential prior to the clinical integration of these LLMs.

Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review

Background

The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field.

Main text

This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking.

Conclusions

Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.

An artificial intelligence platform for the screening and managing of strabismus

OBJECTIVES

Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device.

METHODS

The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC).

RESULTS

A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993.

CONCLUSIONS

Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study

Introduction

Artificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information.

Objective

To develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system's performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics.

Methods and analysis

Our study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system's performance in Shanghai's outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance.

Discussion

Our study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abnormalities, facilitate establishment of a tiered healthcare system, and reduce the burdens on tertiary facilities.

Trial registration

The study was registered in ClinicalTrials.gov on June 25th, 2023 (NCT05930444).

Potential roles of lncRNA MALAT1-miRNA interactions in ocular diseases

Long non-coding RNAs (lncRNAs) are non-protein coding transcripts that are longer than 200 nucleotides in length. LncRNAs are implicated in gene expression at the transcriptional, translational, and epigenetic levels, and thereby impact different cellular processes including cell proliferation, migration, apoptosis, angiogenesis, and immune response. In recent years, numerous studies have demonstrated the significant contribution of lncRNAs to the pathogenesis and progression of various diseases, such as stroke, heart disease, and cancer. Further investigations have shown that lncRNAs have altered expression patterns in ocular tissues and cell lines during pathological conditions. The pathogenesis of various ocular diseases, including glaucoma, cataract, corneal diseases, proliferative vitreoretinopathy, diabetic retinopathy, and retinoblastoma, is influenced by the involvement of specific lncRNAs which play a critical role in the development and progression of these diseases. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a well-researched lncRNA in the context of ocular diseases, which has been shown to exert its biological effects through several signaling pathways and downstream targets. The present review provides a comprehensive summary of the molecular mechanisms underlying the biological functions and roles of MALAT1 in ocular diseases.