With the title Uvemaster: A Mobile App-Based Decision Support System for the Differential Diagnosis of Uveitis, the scientific journal of global ophthalmic reference, reports in August 2017 the scientific validity of Uvemaster
Purpose: To examine the diagnostic accuracy and performance of Uvemaster, a mobile application (app) or diagnostic decision support system (DDSS) for uveitis. The app contains a large database of knowledge including 88 uveitis syndromes each with 76 clinical items, both ocular and systemic (total 6688) and their respective prevalences, and displays a differential diagnoses list (DDL) ordered by sensitivity, specificity, or positive predictive value (PPV).
Methods: In this retrospective case-series study, diagnostic accuracy (percentage of cases for which a correct diagnosis was obtained) and performance (percentage of cases for which a specific diagnosis was obtained) were determined in reported series of patients originally diagnosed by a uveitis specialist with specific uveitis (N = 88) and idiopathic uveitis (N = 71), respectively.
Results: Diagnostic accuracy was 96.6% (95% confidence interval [CI], 93.2–100). By sensitivity, the original diagnosis appeared among the top three in the DDL in 90.9% (95% CI, 84.1–96.6) and was the first in 73.9% (95% CI, 63.6–83.0). By PPV, the original diagnosis was among the top DDL three in 62.5% (95% CI, 51.1–71.6) and the first in 29.5% (95% CI, 20.5–38.6; P < 0.001). In 71 (31.1%) patients originally diagnosed with idiopathic uveitis, 19 new diagnoses were made reducing this series to 52 (22.8%) and improving by 8.3% the new rate of diagnosed specific uveitis cases (performance = 77.2%; 95% CI, 71.1–82.9).
Conclusions: Uvemaster proved accurate and based on the same clinical data was able to detect more cases of specific uveitis than the original clinician only–based method.
Several technologies have been used to apply artificial intelligence and learning machines to the fields of biomedicine and medical diagnosis. The differential diagnosis procedure can be automated using computer-based systems, which are usually referred to as diagnostic decision support systems (DDSS).1–3 In the medical literature, references to DDSS are ever more frequent.4–7 Some of these applications may be found in ophthalmology,8–11 and specifically in uveitis.12–16 Diagnostic decision support systems relate health observations to health knowledge, and therefore help clinicians make adequate decisions for improved health care. A DDSS consists of a knowledge base, inference engine, and user communication. The knowledge base is acquired from the medical literature data, expert consultations, and individual clinical experience.
Uveitis is a major ophthalmologic problem worldwide, with a relatively high prevalence, multiple etiologies, and wide variation in its presentation.17–20 Because of this, its differential diagnosis is difficult especially for nonuveitis experts, generating unnecessary testing costs, delays in initiating correct treatment, and a lack of clear information for patients. The Naming-Meshing-System (NMS) suggested by Smith and Nozik21 is an effective systematic approach to diagnosing uveitis. Based on patient medical history and a physical examination, the clinical findings of the particular case (naming) are compared with the characteristics of specific uveitis syndromes (meshing) to give rise to the differential diagnosis that best matches the set of clinical data. Additional data are then compiled through a tailored approach. According to these authors, the main limitation of NMS consists of the percentage of cases of idiopathic uveitis that cannot be categorized, because of rare entities, masquerade syndromes, and the unpredictable idiosyncratic response of living tissues to invasion by microorganisms and other antigens.
Diagnostic decision support systems can help optimize the clinical management of uveitis.13,15,16,22 Artificial intelligence models offer several benefits, such as their great computer power enabling data for a large number of uveitis syndromes and their different clinical characteristics to be summarized and compared with generate a differential diagnosis.
In this report, we present the Uvemaster (Leading SHT, A Coruña, Spain), a DDSS for uveitis based on a mobile application (app), and assess its diagnostic accuracy and performance.