Decision support systems and applications in ophthalmology: literature and commercial review focused on mobile apps

The Isabel Differential Diagnosis Generator for Orbital Diagnosis

PURPOSE

The Isabel differential diagnosis generator is one of the most widely known electronic diagnosis decision support tools. The authors prospectively evaluated the utility of Isabel for orbital disease differential diagnosis.

METHODS

The terms "proptosis," "lid retraction," "orbit inflammation," "orbit tumour," "orbit tumor, infiltrative" and "orbital tumor, well-circumscribed" were separately input into Isabel and the results were tabulated. Then the clinical details (patient age, gender, signs, symptoms, and imaging findings) of 25 orbital cases from a textbook of orbital surgery were entered into Isabel. The top 10 differential diagnoses generated by Isabel were compared with the correct diagnosis.

RESULTS

Isabel identified hyperthyroidism and Graves ophthalmopathy as the leading causes of lid retraction, but many common causes of proptosis and orbital tumors were not correctly elucidated. Of the textbook cases, Isabel correctly identified 4/25 (16%) of orbital cases as one of its top 10 differential diagnoses, and the median rank of the correct diagnosis was 6/10. Thirty-two percent of the output diagnoses were unlikely to cause orbital disease.

CONCLUSION

Isabel is currently of limited value in the mainstream orbital differential diagnosis. The incorporation of anatomic localizations and imaging findings may help increase the accuracy of orbital diagnosis.

An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application

BACKGROUND

Today, clinical decision support systems based on artificial intelligence can significantly help physicians in the correct diagnosis and quick rapid treatment of endophthalmitis as the most important cause of blindness in emergency diseases. This study aimed to design, develop, and evaluate an intelligent decision support system for acute postoperative endophthalmitis.

METHODS

This study was conducted in 2020-2021 in three phases: analysis, design and development, and evaluation. The user needs and the features of the system were identified through interviews with end users. Data were analyzed using thematic analysis. The list of clinical signs of acute postoperative endophthalmitis was provided to ophthalmologists for prioritization. 4 algorithms support vector machine, decision tree classifier, k-nearest neighbors, and random forest were used in the design of the computing core of the system for disease diagnosis. The acute postoperative endophthalmitis diagnosis application was developed for using by physicians and patients. Based on the data of 60 acute postoperative endophthalmitis patients, 143 acute postoperative endophthalmitis records and 12 non-acute postoperative endophthalmitis records were identified. The learning process of the algorithm was performed on 70% of the data and 30% of the data was used for evaluation.

RESULTS

The most important features of the application for physicians were selecting clinical signs and symptoms, predicting diagnosis based on artificial intelligence, physician-patient communication, selecting the appropriate treatment, and easy access to scientific resources. The results of the usability evaluation showed that the application was good with a mean (± SD) score of 7.73 ± 0.53 out of 10.

CONCLUSION

A decision support system with accuracy, precision, sensitivity and specificity, negative predictive values, F-measure and area under precision-recall curve 100% was created thanks to widespread participation, the use of clinical specialists' experiences and their awareness of patients' needs, as well as the availability of a comprehensive acute postoperative endophthalmitis clinical dataset.

A clinical decision support system for diagnosing and determining severity of dry eye disease

INTRODUCTION

Dry eye and its related symptoms are the most common causes of referrals to the ophthalmology centers. Since people with dry eye may suffer from different levels of the disease severity, this study aimed to develop a clinical decision support system for diagnosing and determining severity of dry eye disease.

METHODS

This research was carried out in two phases in 2020. In the first phase, a questionnaire was designed to identify the most important diagnostic parameters from the cornea specialists' perspectives (n = 37). In the second phase of the research, a clinical decision support system was designed and implemented by using MATLAB software. Finally, the system was evaluated using patient data which were collected in a teaching hospital (n = 50).

RESULTS

The diagnostic parameters for dry eye disease were filamentary keratitis, meibomian gland dysfunction, score of ocular surface disease index, Schirmer's test result, tear meniscus height, tear breakup time, and fluorescein staining score. The system output variables were the diagnosis and severity of dry eye disease at four levels for the right and left eyes, separately. The results of the evaluation study showed that the accuracy, sensitivity and specificity of the system were 96.9%, 97.5%, and 93.7%, respectively.

CONCLUSION

It seems that the system designed in this study can help ophthalmologists to diagnose dry eye disease more accurately and quickly. However, it is recommended to conduct more evaluation studies and include more patients in the future research.

Diagnosis, Classification, and Assessment of the Underlying Etiology of Uveitis by Artificial Intelligence: A Systematic Review

Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.

Development and Validation of a Bayesian Network for Supporting the Etiological Diagnosis of Uveitis

The etiological diagnosis of uveitis is complex. We aimed to implement and validate a Bayesian belief network algorithm for the differential diagnosis of the most relevant causes of uveitis. The training dataset (n = 897) and the test dataset (n = 154) were composed of all incident cases of uveitis admitted to two internal medicine departments, in two independent French centers (Lyon, 2003-2016 and Dijon, 2015-2017). The etiologies of uveitis were classified into eight groups. The algorithm was based on simple epidemiological characteristics (age, gender, and ethnicity) and anatomoclinical features of uveitis. The cross-validated estimate obtained in the training dataset concluded that the etiology of uveitis determined by the experts corresponded to one of the two most probable diagnoses in at least 77% of the cases. In the test dataset, this probability reached at least 83%. For the training and test datasets, when the most likely diagnosis was considered, the highest sensitivity was obtained for spondyloarthritis and HLA-B27-related uveitis (76% and 63%, respectively). The respective specificities were 93% and 54%. This algorithm could help junior and general ophthalmologists in the differential diagnosis of uveitis. It could guide the diagnostic work-up and help in the selection of further diagnostic investigations.

The Use of Mobile Messaging for Telecommunications with Patients in Ophthalmology: A Systematic Review

Background: Communication and concordance between patients and providers are crucial for improved outcomes and disease prevention. Mobile health strategies have been shown to improve patient accessibility and convenience. Mobile messaging is one strategy that has demonstrated varying degrees of effectiveness in patient care. The aim of this review is to investigate methods, outcomes, and conclusions of studies that have assessed mobile messaging interventions in ophthalmology. Methods: A qualitative systematic review of PubMed, Scopus, Web of Science, and Embase databases was conducted to identify studies that investigated the implementation and efficacy of mobile messaging services in ophthalmology practice. Included articles were categorized based on study content: appointment attendance, patient preference and willingness, education, concordance, and other clinical outcomes. Three tools were used to assess for potential bias. Results: Out of a total of 3,655 unique titles retrieved, 15 articles were included in the final qualitative synthesis after abstract and full-text screening. Included studies were published between 2008 and 2020 from seven different countries and across various contexts. All but one study found that the use of mobile messaging in ophthalmology care led to improved process measures or patient outcomes. Evidence for a positive effect was the strongest for appointment follow-up. Survey and feedback data suggest that patients, more so younger patients, are open to mobile message interventions. Conclusion: Mobile messaging interventions can play a role in improving appointment attendance, patient education, and patient practices for ophthalmology patients. Further study is necessary to determine the effectiveness of this tool across various groups and settings.

Android mobile applications in eye care

Google Play Store was used to search for eye care-related applications the android simulator using various general terms related to eye care to review and categorize various interactive eye care-related applications in android platform from the details available in the application website. Data collected from application description and application developer's webpage include target audience, category of apps, estimated number of downloads, average user rating, involvement of eye care professionals in developing the application, and cost of the app. All these data were collected only from the details provided in the application website considering on online user perspective and the developers were not contacted to collect any other details. In total, 475 applications were identified and grouped into 13 categories depending on the type of service the application provide. Out of which, only 107 (22.53%) applications had mentioned about the eye care professional involvement in their design or development of the application. The applications were also stratified according to the target audience, and many had no user rating with very few downloads. The lack of evidence-based principles and standardization of application development should be taken into consideration to avoid its negative impact on the community, especially in eye care.

A Proposed Solution and Future Direction for Blockchain-Based Heterogeneous Medicare Data in Cloud Environment

The healthcare data is an important asset and rich source of healthcare intellect. Medical databases, if created properly, will be large, complex, heterogeneous and time varying. The main challenge nowadays is to store and process this data efficiently so that it can benefit humans. Heterogeneity in the healthcare sector in the form of medical data is also considered to be one of the biggest challenges for researchers. Sometimes, this data is referred to as large-scale data or big data. Blockchain technology and the Cloud environment have proved their usability separately. Though these two technologies can be combined to enhance the exciting applications in healthcare industry. Blockchain is a highly secure and decentralized networking platform of multiple computers called nodes. It is changing the way medical information is being stored and shared. It makes the work easier, keeps an eye on the security and accuracy of the data and also reduces the cost of maintenance. A Blockchain-based platform is proposed that can be used for storing and managing electronic medical records in a Cloud environment.

A mobile decision support system for red eye diseases diagnosis: experience with medical students

A good primary health care is the base for a better healthcare system. Taking a good decision on time by the primary health care physician could have a huge repercussion. In order to ease the diagnosis task arise the Decision Support Systems (DSS), which offer counselling instead of refresh the medical knowledge, in a profession where it is still learning every day. The implementation of these systems in diseases which are a frequent cause of visit to the doctor like ophthalmologic pathologies are, which affect directly to our quality of life, takes more importance. This paper aims to develop OphthalDSS, a totally new mobile DSS for red eye diseases diagnosis. The main utilities that OphthalDSS offers will be a study guide for medical students and a clinical decision support system for primary care professionals. Other important goal of this paper is to show the user experience results after OphthalDSS being used by medical students of the University of Valladolid. For achieving the main purpose of this research work, a decision algorithm will be developed and implemented by an Android mobile application. Moreover, the Quality of Experience (QoE) has been evaluated by the students through the questions of a short inquiry. The app developed which implements the algorithm OphthalDSS is capable of diagnose more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The students find the diseases' information presented very valuable, the appearance is adequate, it is always available and they have ever found what they were looking for. Furthermore, the students think that their quality of life has not been improved using the app and they can do the same without using the OphthalDSS app. OphthalDSS is easy to use, which is capable of diagnose more than 30 ocular diseases in addition to be used as a DSS tool as an educational tool at the same time.

eHealth: past and future perspectives

eHealth is an umbrella term incorporating any area that combines healthcare and technology to improve efficiencies and reduce costs. The ultimate goal of eHealth is to rationalize treatment selection to improve patient safety and outcomes. Telemedicine, first used in the 1920s, is the oldest form of eHealth. The introduction of broadband Internet, followed by wireless technologies, has allowed an explosion of mHealth applications within this field. Wearable technologies, such as smartwatches, are now being used for diagnostics and patient monitoring. Challenges remain to develop reusable Clinical Decision Support systems that will streamline the flow of data from clinical laboratories to point of care. This review explores the history of eHealth, and describes some of the remaining integration and implementation challenges.

Experiences and Results of Applying Tools for Assessing the Quality of a mHealth App Named Heartkeeper

Currently, many incomplete mobile apps can be found in the commercial stores, apps with bugs or low quality that needs to be seriously improved. The aim of this paper is to use two different tools for assessing the quality of a mHealth app for the self-management of heart diseases by the own patients named Heartkeeper. The first tool measures the compliance with the Android guidelines given by Google and the second measures the users' Quality of Experience (QoE). The results obtained indicated that Heartkeeper follows in many cases the Android guidelines, especially in the structure, and offers a satisfactory QoE for its users, with special mention to aspects such as the learning curve, the availability and the appearance. As a result, Heartkeeper has proved to be a satisfactory app from the point of view of Google and the users. The conclusions obtained are that the type of tools that measure the quality of an app can be very useful for developers in order to find aspects that need improvements before releasing their apps. By doing this, the number of low-quality applications released will decrease dramatically, so these techniques are strongly recommended for all the app developers.