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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Artificial Intelligence | Research

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

Authors: Mahdi Shaeri, Nasser Shoeibi, Seyedeh Maryam Hosseini, Fatemeh Rangraze Jeddi, Razieh Farrahi, Ehsan Nabovati, Azam Salehzadeh

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

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.

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Title: An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application
Authors: Mahdi Shaeri
Nasser Shoeibi
Seyedeh Maryam Hosseini
Fatemeh Rangraze Jeddi
Razieh Farrahi
Ehsan Nabovati
Azam Salehzadeh
Publication date: 01-12-2023
Publisher: BioMed Central
Keyword: Artificial Intelligence
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02214-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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