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

Abstract

Abstract This study aimed to design, develop, and evaluate an intelligent decision support system for acute postoperative endophthalmitis. 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. The k-nearest neighbors' algorithm was 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, 3693 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. 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. All-round participation and using the experiences of clinical specialists, and their awareness of patient needs, as well as the availability of comprehensive acute postoperative endophthalmitis clinical dataset led to the design of a decision support system with accuracy, precision and sensitivity above 90%.