Artificial Intelligence in Pressure Injury Diagnosis: A Critical Appraisal for Clinical Practice

Abstract

Significance:

Pressure injury is one of the most common health problems among hospitalized patients worldwide, and accurate and timely diagnosis is crucial for its treatment. Research on the application of artificial intelligence in the diagnosis of pressure injury is increasing, but there is currently no comprehensive meta-analysis to evaluate the accuracy of artificial intelligence in diagnosing different pressure injury stages.

Recent Advances:

This study synthesizes evidence on artificial intelligence diagnosis of pressure injury, focusing on evaluating diagnostic performance across different stages using core metrics including sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve.

Critical Issues:

Key findings from 21 included studies (12 contributing 47 eligible datasets) indicate high overall diagnostic accuracy of artificial intelligence for pressure injury, with sensitivity of 0.74 (95% confidence interval [CI]: 0.69–0.78), specificity of 0.93 (95% CI: 0.91–0.94), and area under the SROC curve of 0.92 (95% CI: 0.90–0.94). Moreover, the area under the SROC curve varies across different stages of pressure injury, with area under the curve values for stage 1, stage 2, stage 3, stage 4, unstageable, and deep tissue pressure injury of 0.95 (0.93–0.97), 0.85 (0.82–0.88), 0.88 (0.84–0.90), 0.94 (0.92–0.96), 0.96 (0.94–0.97), and 0.98 (0.96–0.99), respectively.

Future Directions:

Artificial intelligence models based on pressure injury image data show substantial potential for clinical application in pressure injury diagnosis. However, the need for high-quality studies with rigorous reporting and external validation remains critical to address current limitations and advance clinical translation.