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Diagnostic accuracy and deployment readiness of AI for burn depth assessment across imaging modalities: a PRISMA-DTA
Yiyun Wang1, Yanxia Jiang1, Yutong Zhang1
1Department of Burns and Plastic Surgery, The 909th Hospital, School of Medicine, Xiamen University, No. 269 Zhanghua Middle Road, Xiangcheng District, Zhangzhou, Fujian 363000, China.
Background:
AI-based burn depth assessment is rapidly emerging, yet evidence for diagnostic accuracy, generalizability, and deployment readiness remains unclear.
Methods:
We performed a PRISMA-DTA-aligned systematic review and meta-analysis. We synthesized accuracy outcomes and extracted deployment-relevant features, including validation design, reference-standard family, analytic unit, and subgroup reporting. Six studies (4,897 observations; mixed image-, wound-, and patient-level units) were included; four studies (N = 2541) formed the prespecified primary DTA dataset. We planned bivariate random-effects models where feasible and conducted exploratory subgroup analyses by imaging modality, skin tone, and age. Two additional studies were included only in exploratory analyses after prespecified approximate 2 × 2 reconstruction.
Results:
In the primary dataset, the prespecified bivariate model did not converge because of sparse/extreme 2 × 2 patterns and limited study numbers; therefore, no pooled sensitivity or specificity was generated. Study-level sensitivity ranged from 0.50 to 1.00 and specificity from 0.54 to 0.97. In the expanded exploratory dataset (6 studies), pooled sensitivity was 0.924 (95% CI 0.788-0.975) and specificity 0.877 (95% CI 0.701-0.956), but these estimates are hypothesis-generating because they rely partly on approximately reconstructed 2 × 2 data. Exploratory descriptive analyses suggested possible modality-related variation and lower specificity in darker skin, although subgroup evidence was sparse and non-confirmatory; pediatric evidence was limited to a single within-study stratum.
Conclusions:
The evidence base is insufficient for deployment-ready use of AI burn depth assessment. The primary dataset did not support hierarchical pooling, and exploratory pooled estimates should be interpreted cautiously because they rely on reconstructed 2 × 2 data and mixed analytic units. More importantly, this review clarifies the evidence gaps separating promising algorithmic performance from deployable clinical decision support. Future studies should prioritize standardized reference standards, patient-level external validation in independent institutions, and reporting that supports safe integration into clinical workflows.
