小児肺結核の画像診断における人工知能の貢献

Pediatric tuberculosis (TB) remains a diagnostic challenge in Brazil and worldwide. The Brazilian Ministry of Health recommends a clinical scoring system (S-MoH) for children and adolescents with suspected TB. Interpretation of radiographs within this scoring system may require specialist input. AI-based systems, such as CAD4TB (Delft Imaging Systems B.V.), approved by the WHO for adults, are not yet recommended for standalone use in children under 15 years of age. A retrospective study was conducted at a pediatric institute from January 31, 2017, to January 29, 2025, including 179 patients aged 0-14 years with pulmonary TB or other diseases. CAD4TBv7.1 analyzed chest radiographs using two cutoff points established by Youden's index: 53.48 for analyses against the S-MoH score and 53.89 for analyses against microbiological confirmation. Results were compared with both microbiological confirmation and S-MoH score. Among the 179 participants, 61 (34.1%) had TB, 25 of which were microbiologically confirmed. CAD4TBv7.1 showed an area under the ROC curve (AUROC) of 0.71, with a sensitivity of 52% and a specificity of 86.3% compared with microbiological diagnosis. Against S-MoH, AUROC was 0.59, with a sensitivity of 34.43% and a specificity of 86.44%. CAD4TBv7.1 demonstrated low sensitivity and high specificity, particularly regarding its overall discriminative capacity. Thus, CAD4TBv7.1 emerges as a promising complementary screening tool for pediatric TB. Although its standalone use is not yet recommended, it may complement S-MoH in settings lacking radiologists. Investments in AI must be accompanied by consistent pediatric validation and strategies that combine technological innovation with traditional and cost-effective clinical approach.