A health terminological system for inherited retinal diseases: Content coverage evaluation and a proposed classification.

PURPOSE: To present a classification of inherited retinal diseases (IRDs) and evaluate its content coverage in comparison with common standard terminology systems. METHODS: In this comparative cross-sectional study, a panel of subject matter experts annotated a list of IRDs based on a comprehensive review of the literature. Then, they leveraged clinical terminologies from various reference sets including Unified Medical Language System (UMLS), Online Mendelian Inheritance in Man (OMIM), International Classification of Diseases (ICD-11), Systematized Nomenclature of Medicine (SNOMED-CT) and Orphanet Rare Disease Ontology (ORDO). RESULTS: Initially, we generated a hierarchical classification of 62 IRD diagnosis concepts in six categories. Subsequently, the classification was extended to 164 IRD diagnoses after adding concepts from various standard terminologies. Finally, 158 concepts were selected to be classified into six categories and genetic subtypes of 412 cases were added to the related concepts. UMLS has the greatest content coverage of 90.51% followed respectively by SNOMED-CT (83.54%), ORDO (81.01%), OMIM (60.76%), and ICD-11 (60.13%). There were 53 IRD concepts (33.54%) that were covered by all five investigated systems. However, 2.53% of the IRD concepts in our classification were not covered by any of the standard terminologies. CONCLUSIONS: This comprehensive classification system was established to organize IRD diseases based on phenotypic and genotypic specifications. It could potentially be used for IRD clinical documentation purposes and could also be considered a preliminary step forward to developing a more robust standard ontology for IRDs or updating available standard terminologies. In comparison, the greatest content coverage of our proposed classification was related to the UMLS Metathesaurus.

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review.

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.

Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review.

We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.

Decision fusion in healthcare and medicine: a narrative review.

OBJECTIVE: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. BACKGROUND: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. METHODS: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. CONCLUSIONS: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Home quarantine is a useful strategy to prevent the coronavirus outbreak: Identifying the reasons for non-compliance in some Iranians.

INTRODUCTION: The coronavirus outbreak has become a worrying issue and some people refuse to stay at home. Therefore, this study aims to identify the reasons behind some Iranian people's refusal to stay at home to prevent further virus transmission. METHOD: This cross-sectional study was conducted on postgraduate students in Iran. A questionnaire was designed based on 50 experts' opinions by using the Delphi method and 203 students completed the designed questionnaire in telegram groups. RESULTS: 35% of participants were upper 30 years of age, 70.4% were female, 74.4% had no coronavirus infection among their relatives, and 54.7% of them were Ph.D. candidates. The relations between "unclear accountability of events by some officials" and age as well as "failure to provide dissenting viewpoints and critical comments" and age were statistically significant (p = 0.027، p = 0.014). Moreover the relation between coronavirus infected relative and "persistent beliefs" was statistically significant (p = 0.014). The Chi-square test showed that gender, degree, resident and education province did not affect questions answering. The greatest agreement with questions is as following: lack of real situation understanding; 89.7%, people's livelihoods, and lack of government planning for low-income groups support; 86.7%, lack of people's knowledge concerning the coronavirus; 80.8%, lack of communicative educations for crisis situations; 79.8%, false assurance as well as minimizes the risks; 78.3%. CONCLUSION: Identifying the non-compliance factors with health recommendations can guide health care providers and managers to implementation of beneficial intervention.