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BACKGROUND: The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. OBJECTIVE: The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. METHODS: The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. RESULTS: A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. CONCLUSIONS: This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. TRIAL REGISTRATION: PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.
Assuntos
Algoritmos , Inteligência Artificial , Humanos , Medição da Dor , Área Sob a Curva , DorRESUMO
[This corrects the article DOI: 10.2196/51250.].
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Currently, the global healthcare market is increasing at high speed with the impendent global aging issue. Healthcare Industry 4.0 has emerged as an efficient solution towards the aging issue since it was integrated with ubiquitous medical sensors, big health processing platform, high bandwidth, speed technologies, and medical services, etc. It is believed to fulfil the requirement of the tremendously growing health market. The acquisition of medical data acts as the dominant precondition to implement the Healthcare Industry 4.0. In the same way, the widely available smartphone could serve as the best biomedical information collect station. In this study, a smartphone-powered photochemical dongle is demonstrated to precisely estimate blood creatinine from the fingertip blood, which works as a highly compact reflectance spectral analyzer with an enzymatically dry chemical test strip. Comparing with conventional laboratory facility for the evaluation and treatment of chronic kidney disease (CKD), it implemented the platform of point care with agreed accuracy. In order to estimate the efficiency of treatment and recovery of the CKD, the proposed photochemical dongle would provide a flexible and rapid platform for point of care. Furthermore, the proposed measured technology is very promising method for remote CKD management.