Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.

PURPOSE: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools. METHODS: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included. RESULTS: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data. CONCLUSION: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review.

PURPOSE: This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. METHODS: A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. RESULTS: A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. CONCLUSION: The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.

Impact on caregivers of patients with eye diseases: systematic review and meta-analysis.

OBJECTIVE: The burden on caregivers of patients with eye disease is exacerbated by competing priorities and their own advancing age. The objective of this paper is to identify and characterize the burden of caregiving for patients with eye diseases. DESIGN: Systematic review and meta-analysis. METHODS: A comprehensive review of the literature was conducted accessing the main databases up to March 25, 2021: Medline (Ovid), Embase, CINAHL, Cochrane Library, PsycINFO, and PubMed. Studies included referred to the burden experienced by caregivers of adults with any eye disease. Review studies, editorials, commentaries, opinion studies, and single case reports were excluded as well as studies that described only the quality of life and (or) burdens of the care recipient. Two independent reviewers carried 2-level screening. Risk of bias assessment was determined according to the Modified Downs and Black Checklist. Demographic data and measures of burden were extracted and tabulated. RESULTS: In total, 2421 articles were identified after duplicate removal; 17 progressed to data extraction after full-text screening, and 7 were included in the meta-analysis. Overall, caregiving was associated with significant burden (65%; 95% CI, 12%-17%). Meta-analysis indicated significant severe burden (24%; 95% CI, 3%-45%), moderate burden (18%; 95% CI, 9%-28%), and mild burden (34%; 95% CI, 7%-61%) on caregivers of visually impaired patients. CONCLUSIONS: Awareness of the psychosocial impact and risk of severe burden experienced by caregivers of eye disease patients is required to aid health care providers to develop proactive interventions for both providers and recipients of care.

Emerging applications of bioinformatics and artificial intelligence in the analysis of biofluid markers involved in retinal occlusive diseases: a systematic review.

PURPOSE: To review the literature on the application of bioinformatics and artificial intelligence (AI) for analysis of biofluid biomarkers in retinal vein occlusion (RVO) and their potential utility in clinical decision-making. METHODS: We systematically searched MEDLINE, Embase, Cochrane, and Web of Science databases for articles reporting on AI or bioinformatics in RVO involving biofluids from inception to August 2021. Simple AI was categorized as logistics regressions of any type. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Among 10,264 studies screened, 14 eligible articles, encompassing 578 RVO patients, met the inclusion criteria. The use and reporting of AI and bioinformatics was heterogenous. Four articles performed proteomic analyses, two of which integrated AI tools such as discriminant analysis, probabilistic clustering, and string pathway analysis. A metabolomic study used AI tools for clustering, classification, and predictive modeling such as orthogonal partial least squares discriminant analysis. However, most studies used simple AI (n = 9). Vitreous humor sample levels of interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and aqueous humor levels of intercellular adhesion molecule-1 and IL-8 were implicated in the pathogenesis of branch RVO with macular edema. IL-6 and VEGF may predict visual acuity after intravitreal injections or vitrectomy, respectively. Metabolomics and Kyoto Encyclopedia of Genes and Genomes enrichment analysis identified the metabolic signature of central RVO to be related to lower aqueous humor concentration of carbohydrates and amino acids. Risk of bias was low or moderate for included studies. CONCLUSION: Bioinformatics has applications for analysis of proteomics and metabolomics present in biofluids in RVO with AI for clinical decision-making and advancing the future of RVO precision medicine. However, multiple limitations such as simple AI use, small sample volume, inconsistent feasibility of office-based sampling, lack of longitudinal follow-up, lack of sampling before and after RVO, and lack of healthy controls must be addressed in future studies.

The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review.

Purpose: This study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases. Methods: A comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases. Results: After screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant. Conclusion: This study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.

Ocular adverse effects of bladder medication: a systematic review.

BACKGROUND: With the ageing population, lower urinary tract symptoms are becoming more prevalent with an estimate that by 2025, 52 million adults in the USA will be affected. After lifestyle modifications fail to resolve symptoms, second-line therapy with medications is often recommended by both the European Association of Urology and the American Urological Association. Considering the vulnerability of older patients to co-morbidities, physicians must be more aware of adverse side effects. This study aims to identify a linkage between common overactive bladder and interstitial cystitis medication and adverse ocular symptoms. METHODS: A comprehensive literature search was conducted in MEDLINE, EMBASE, CINAHL, PsycInfo, and HealthSTAR alongside a grey literature search in clinicaltrials.gov to include all articles relating to bladder medication and vision-threatening loss. Covidence review software was utilised to conduct the systematic review. RESULTS: In total, 222 articles were screened, and 23 articles met the inclusion criteria. Comprehensive coverage of 10 available medications was analysed. All medications reported adverse vision effects stratified over 15 categories. The most common adverse effect was reported to be blurred vision (n = 12 studies). Mirabegron had the most number of adverse types of ocular symptoms that covered 6 categories. Cizolirthine Citrate and Elocatitol had the least amount of ocular side effects reported. From the total of 8459 patients that were treated for either overactive bladder syndrome or interstitial cystitis with oral medications, 422 reported adverse vision effects. CONCLUSIONS: This review suggests that ocular safety should be assessed in patients requiring systematic drug therapy in order to guide future research, focussing on long-term tolerability.