Three-dimensional deep-learning age estimation from the fourth rib on high-resolution CT: An evaluative likelihood-ratio approach

For four decades, age estimation from the fourth rib has rested on İşcan's visual phase system, which requires the physical bone and returns an ordinal category, not a calibrated probability. We developed a three-dimensional deep-learning pipeline, automated downstream of AI-assisted segmentation, that estimates age from the whole rib on computed tomography (CT) and reports a likelihood ratio. Right fourth ribs from thin-section (1 mm) chest CT of 453 living adults (225 men, 228 women; 18.0-99.8 years) were segmented, posed canonically and encoded as four representations: metric multi-view RGB-D renderings and a point cloud of the sternal end, a point cloud of the complete rib, and twenty morphometric scalars. Sex-specific networks trained by label-distribution learning were evaluated on a frozen hold-out set (three seeds). The selected estimators reached a mean absolute error of 10.6 years in men and 10.4 in women (45-46% below a mean-age predictor; 9.0 and 8.4 years between ages 18 and 70). Likelihood ratios were informative at every threshold from 30 to 70 years (Cllr < 1), sex was classified with an area under the ROC curve of 0.966, and intra-observer reproducibility was ICC 0.872, limited chiefly by the canonical pose. The age signal was not confined to the sternal extremity examined by phase scoring: in women the complete rib outperformed the sternal end (10.4 versus 14.6 years); in men the two were equivalent, consistent with sex-specific standards. Because 1 mm sections cannot resolve İşcan's fine relief, higher-resolution surface capture is a priority for future work.