Reliability of an AI-Powered Application Across Different Mobile Devices for Assessment of Chronic Wounds.

Abstract

Objective: Evaluate the inter- and intrarater reliability of a wound assessment tool in iPhone 12 and 13 mini modalities against a validated iPad mini/Structure Sensor configuration. Approach: We assessed a wound measurement application (eKare inSight^®) for result consistency in patients presenting with wounds. Assessments were analyzed using a two-way analysis of variance. Intraclass correlation coefficient (ICC) was computed for intrarater (ICC_1,1) and inter-rater (ICC_2,1) analysis using a two-way random effects model. Paired t-test assessed the statistical difference between measurement methods. Results: Forty-two lesions were analyzed with surface areas ranging from 0.2 to 23 cm² (average 4.33 ± 5.44 cm²). A high level of reliability was observed for repeat wound area measurements by the same examiner (ICC_1,1 = 0.997) and between examiners with iPhone 13 mini (ICC_2,1 = 0.998). There was no significant difference between iPhone 12 and iPad mini/Structure Sensor (p = 0.78) or between iPhone 13 mini and iPhone 12 (p = 0.22). Minimal difference existed between iPhone 13 mini and iPad mini/Structure Sensor (p = 0.049, Cohen's d = 0.01). Innovation: Increased pervasiveness of smartphones in clinical care, coupled with advances in smartphone imaging and machine learning, allows for a potential solution to the problem of fast and accurate wound measurements. The application investigated produces wound measurement results quickly and with demonstrated accuracy. It does not require a calibration sticker or reference marker and allows for automatic wound boundary delineation. Conclusion: The results of this study suggest that a digital planimetry mobile application may offer high levels of reliability across devices and users.