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.

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.

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.

Risk Factors Associated with Keratoconus in an Iranian Population

Purpose

To determine associated factors for keratoconus (KCN) in the Iranian population.

Methods

In this retrospective case-control study, 100 KCN patients and 200 age- and sex-matched individuals, who were either candidates for photorefractive keratectomy or healthy referrals from the Torfeh Eye Hospital, were included as the case and control groups, respectively. KCN patients were all registered at the Iranian National Registry of Keratoconus (KCNRegⓇ). Demographic characteristics, patients' symptoms and their habits, as well as systemic and ocular disorders were documented. Clinical examinations included best corrected visual acuity (BCVA) and refractive error measurements, biomicroscopic examination, and corneal imaging.

Results

In this case group, the frequency of mild, moderate, and severe KCN was 38%, 28%, and 34%, respectively. Parental consanguinity (odds ratio [OR] = 1.758, P = 0.029), positive familial history in patients' first degree (OR = 12.533, P < 0.001) and second degree (OR = 7.52, P < 0.001) relatives, vernal keratoconjunctivitis (OR = 7.510, P = 0.003), severe eye rubbing (OR = 10.625, P < 0.001), and systemic diseases including migraine, hypertension, and thyroid disease (OR = 6.828, P = 0.021) were found as associated factors for KCN. Lesser frequency of KCN was observed in patients with Fars ethnicity (OR = 0.583, P = 0.042), with higher levels of wealth indices (OR = 0.31, P < 0.001) and higher levels of education (OR = 0.18, P = 0.024).

Conclusion

Severe eye rubbing, vernal keratoconjunctivitis, parental consanguinity and positive familial history of KCN, low socioeconomic status, and low levels of education were significantly associated with KCN in our study population.

The Influence of Near Vision Tasks on Intraocular Pressure in Normal Subjects and Glaucoma Patients

Purpose

To investigate the effect of static accommodative tasks on intraocular pressure (IOP) of glaucomatous and normal eyes.

Methods

Four groups of subjects categorized as primary open-angle glaucoma (POAG), primary angle-closure suspects (PACS), normal age-matched controls, and normal young adults (NYA; age < 40 years) were enrolled. The baseline IOPs were measured after the subjects were looking at a distant target for 15 min. Static accommodation was obtained by execution of near vision tasks (reading at 33 cm in daylight [300 lux] for 60 min). IOPs were measured at 15, 30, 45, and 60 min intervals while accommodating and then measured again after 15 min of relaxing accommodation while looking at a distant target.

Results

One-hundred and eighteen eyes of 98 subjects were recruited. The study groups consisted of the following categories: 25 POAG (46 eyes), 24 PACS (47 eyes), 25 matched controls (50 eyes), and 24 NYA (48 eyes). Within all groups, the mean IOP decreased throughout the accommodation period at all time points. Maximum IOP reduction after accommodation was detected at the 30-min time among the POAG subjects, at the 45-min time in the PACS and matched control groups, and at 15 min after the relaxation of accommodation in the NYA group. IOP reduction levels showed no statistically significant difference among POAG, PACS, and the normal matched groups in their response to accommodation. However, NYA had significantly lower IOP and greater IOP reduction after the resting period (relaxation of accommodation).

Conclusion

Static accommodative tasks can significantly reduce IOP in normal, POAG, and PACS individuals. Encouraging glaucoma patients to practice periodical near vision tasks could be viewed as an adjunctive measure for glaucoma management.

Psychological Impact of COVID- 19 on the Ophthalmologists in Iran

Purpose

To identify the psychological impact of coronavirus disease on ophthalmologists practicing in Iran between August and December 2020.

Methods

In this cross-sectional online survey, a standard Patient Health Questionnaire- 9 (PHQ- 9) was completed by 228 ophthalmologists who were practicing in Iran. The PHQ- 9 questionnaire was revised by adding two additional questions specifically applicable for the assessment of the psychological impact of coronavirus disease on the Iranian ophthalmologists. An organized classification regarding the assessment of different depression severities identified as no (0–4), mild (5–9), moderate (10–14), or severe (15–21) was then considered for data analysis.

Results

The mean age of our participants was 49.0 ± 15.61 years and the majority of them (67.1%) were male. Depression was discovered in 73.68% (n = 168) with different severities ranging from mild (n = 61, 26.75%), moderate (n = 63, 27.63%), and severe (n = 44, 19.3%). It was found that participants with depression were older as compared to those without depression (P = 0.038). Higher percentages of severe depression were noticed in the high-risk regions contaminated with corona virus as compared to the other low-risk regions (P = 0.003). Based on multivariable models, we determined that ophthalmologists who were somewhat concerned about their training/ profession (OR: 0.240; 95% CI: 0.086–0.672; P = 0.007) and those with no concerns about their income had lower association with depression (OR: 0.065; 95% CI: 0.005–0.91; P = 0.042).

Conclusion

High prevalence of depression was observed among older aged Iranian ophthalmologists living in high-risk contaminated regions who possessed serious concerns with respect to their training/profession and income. It is recommended that the health policymakers of Iran pay more attention to the ophthalmologists who experience the aforementioned factors.

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.

Developing a Questionnaire for Mobile Phone Usage Pattern Among University Students Using the Delphi Method

Introduction: The rate of mobile phone use among people, especially young people is increasing. The proper use of mobile phone for utilizing the advantages and stay away from its complications is essential. To obtain a model and how to use mobile phone will facilitate planning for preventing complications. So, in this article, questionnaire development with aimed at examining the pattern of mobile phone use among students of Iranian Universities.Methods: In this study a self-administered questionnaire was designed based on a literature review in PubMed, EMBASE, Science Direct, and Google Scholar database and using 2 rounds of the Delphi method with the presence of 10 experts from different fields.Result: In the first Delphi round 6 questions were obtained and in the second round 15 questions were confirmed. The mean of Content Validity Ratio and Content Validity Index for the questionnaire was 93.32 and 92.70, respectively. A questionnaire was designed and developed according to the purpose.Conclusions: Using the designed questionnaire, the mobile usage pattern among student universities can be examined and solutions can be considered for them. This can prevent further consequence.

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.

Applications of Framework In Health Care: A Survey

Introduction: The use of healthcare frameworks and, in particular, policy makers is crucial for designing and evaluating systems. Frameworks provide the ability to measure and compare health system functions in different countries in order to make better and more meaningful decisions, to make comparisons within and between countries, identifying gaps, and sharing information. Researchers also have the ability to use the dimensions of the frameworks to measure progress over time. Due to the importance of the subject, the purpose of this study is to describe the framework concepts and the introduction of framework applications in the field of health care.Material and Methods: This study is based on a search of the ProQuest, PubMed, Google Scholar, Science Direct, Scopus, IranMedex, Irandoc, Magiran, ParsMedline and Scientific Information Database (SID) databases, as well as the study of specialized keyword web sites and the standard was done. After a thorough study, 50 sources were selected according to the study objectives and were used to formulate the final article.Results: The framework can be used to manage health system investments, identify important research areas in the field of health, and define new and useful research.Conclusion: Given the importance of the health framework, the need to provide a framework for other critical health care sectors is essential.

BigData Analysis in Healthcare: Apache Hadoop , Apache spark and Apache Flink

Introduction: Health care data is increasing. The correct analysis of such data will improve the quality of care and reduce costs. This kind of data has certain features such as high volume, variety, high-speed production, etc. It makes it impossible to analyze with ordinary hardware and software platforms. Choosing the right platform for managing this kind of data is very important. The purpose of this study is to introduce and compare the most popular and most widely used platform for processing big data, Apache Hadoop MapReduce, and the two Apache Spark and Apache Flink platforms, which have recently been featured with great prominence.Material and Methods: This study is a survey whose content is based on the subject matter search of the Proquest, PubMed, Google Scholar, Science Direct, Scopus, IranMedex, Irandoc, Magiran, ParsMedline and Scientific Information Database (SID) databases, as well as Web reviews, specialized books with related keywords and standard. Finally, 80 articles related to the subject of the study were reviewed.Results: The findings showed that each of the studied platforms has features, such as data processing, support for different languages, processing speed, computational model, memory management, optimization, delay, error tolerance, scalability, performance, compatibility, Security and so on. Overall, the findings showed that the Apache Hadoop environment has simplicity, error detection, and scalability management based on clusters, but because its processing is based on batch processing, it works for slow complex analyzes and does not support flow processing, Apache Spark is also distributed as a computational platform that can process a big data set in memory with a very fast response time, the Apache Flink allows users to store data in memory and load them multiple times and provide a complex Fault Tolerance mechanism Continuously retrieves data flow status.Conclusion: The application of big data analysis and processing platforms varies according to the needs. In other words, it can be said that each technology is complementary, each of which is applicable in a particular field and cannot be separated from one another and depending on the purpose and the expected expectation, and the platform must be selected for analysis or whether custom tools are designed on these platforms.