When investigators recover human remains after a disaster, a plane crash, or years of exposure in a shallow grave, two of the most fundamental questions they must answer are deceptively simple: who was this person, and how old were they? Traditionally, answering those questions has required the trained eye of a forensic anthropologist, who inspects the pelvis, the skull, or other bones for telltale signs of sex and age. But those structures are often fragmented, missing, or too poorly preserved to yield reliable answers. A new study from Japan suggests that a single bone—the femur, or thigh bone—combined with cutting-edge artificial intelligence may be enough to build a biological profile automatically, with accuracy that rivals or exceeds conventional methods.

The research, published in the International Journal of Legal Medicine, was led by Suguru Torimitsu of the University of Tokyo and Chiba University, together with collaborators including Eiryo Kawakami and Yohsuke Makino. The team set out to test whether a so-called foundation model—a large, pretrained neural network of the kind that has transformed medical imaging—could simultaneously estimate both sex and age from three-dimensional computed tomographic images of the femur. Their cohort was drawn from postmortem CT scans of 1,489 Japanese individuals, 1,003 males and 486 females, aged between 20 and 90 years, all of whom underwent whole-body PMCT followed by forensic autopsy at the two universities between May 2015 and November 2025.

The technical pipeline behind the study is a showcase of modern deep learning practice. First, the femora were automatically segmented from whole-body scans using the TotalSegmentator library, which isolates anatomical structures without manual tracing. Each segmented femur was then converted into a three-dimensional volume of 112 by 112 by 112 voxels, with voxels outside the bone mask replaced by air-equivalent values to suppress background noise, and intensity normalized using percentile-based scaling. Rather than feeding the model selected two-dimensional slices, the researchers processed the entire volumetric bone, allowing the network to learn from the complete three-dimensional morphology of the femur.

At the heart of the system sits 3DINO, a three-dimensional Vision Transformer foundation model pretrained on large-scale medical imaging data. Vision Transformers work by dividing an image into patches and encoding the relationships between those patches through attention mechanisms, which let the model weigh the importance of different regions dynamically. To adapt this general-purpose backbone to the forensic task, the team used low-rank adaptation, or LoRA, a parameter-efficient fine-tuning technique in which only small, low-rank matrices inserted into the network are updated during training while the original pretrained weights remain frozen. This approach dramatically reduces the number of trainable parameters, lowering the risk of overfitting on a dataset of modest size and shortening training time.

The framework was trained in a multitask learning configuration, meaning a single shared feature-extraction backbone fed two separate output heads: one performing binary classification for sex, and one performing regression for age, with predicted values scaled to the physiological range through a sigmoid function. The total loss was a weighted sum of the classification and regression losses, enabling joint optimization. The intuition is biologically sound: sex-related morphology and age-related structural change are correlated aspects of the same skeleton, so learning them together can produce richer internal representations than learning either in isolation. Training used the AdamW optimizer with a cosine-annealing learning-rate schedule, data augmentation including random cropping and affine transformations, and early stopping to prevent overfitting. The dataset was split into a training-and-validation cohort and an independent test cohort in a four-to-one ratio, with stratified sampling by age.

The results, evaluated on the independent test set, were striking. For sex estimation, the model achieved balanced accuracies of 0.897 for the left femur and 0.929 for the right femur, with area under the receiver operating characteristic curve values of 0.979 and 0.988 respectively—figures the authors describe as favorable compared with previous reports. Earlier CT-based morphometric studies in Japanese populations had produced accuracies of roughly 73 percent from femoral length alone, rising to around 88 to 90 percent when combining measurements such as femoral epicondylar breadth and head diameter. The deep learning approach, which captures complex nonlinear morphological patterns beyond what linear measurements can express, matched or surpassed those benchmarks without any manual feature extraction. Calibration metrics, including Brier scores and expected calibration error, were both below 0.1, indicating that the model’s predicted probabilities were trustworthy rather than merely accurate on average.

Age estimation proved harder, as it always does in forensic anthropology, where adult skeletal indicators are notoriously imprecise. The models achieved a mean absolute error of approximately eight years and a root mean square error of approximately ten years. Notably, performance was better for female individuals than for males, with lower error values and a higher coefficient of determination in the female subgroup. Bland–Altman analysis revealed mean biases close to zero, but also a familiar statistical signature: the model tended to overestimate the ages of younger individuals and underestimate those of older ones, a regression toward the mean that plagues most skeletal aging methods. The authors suggest that age-related morphological variation in the femur may be more consistent in females, while greater interindividual variability in males reduces accuracy; they also note that postmenopausal estrogen deficiency may shape female femoral morphology in ways the model can exploit.

Perhaps the most scientifically intriguing findings came from the attention maps, which reveal where in the bone the network was looking when it made its decisions. For sex estimation, attention concentrated overwhelmingly in the distal femur—the lower end near the knee—regardless of age group, a result consistent with decades of osteometric research showing that the epicondylar breadth of the distal femur is among the most sexually dimorphic measurements available. For age estimation, however, attention shifted to the femoral shaft, the long diaphyseal midsection, which in the right femur accounted for the highest attention in nearly all cases across both age groups. This dissociation suggests the model is genuinely learning distinct biological signals rather than relying on a single anatomical shortcut, and it points forensic researchers toward diaphyseal features as an underexplored source of age-related information.

One unexpected observation was a laterality effect: the right femur model consistently outperformed the left, and attention maps from the left femur showed greater variability in the contributing regions, particularly among older individuals. The authors caution that the reasons remain unclear and that further work is needed to determine whether this reflects biology, imaging conditions, or methodological artifacts. They are equally candid about other limitations. The study included only intact femora from Japanese individuals, so it remains unknown whether the models work on fragmented or decomposed remains—precisely the conditions most common in real forensic casework. Skeletal sexual dimorphism also varies across populations, meaning standards derived from Japanese data may not transfer directly elsewhere, and factors such as nutrition, lifestyle, occupation, and cultural habits were not examined.

Even with those caveats, the implications are considerable. Automated segmentation and foundation-model feature extraction remove the reliance on manual measurements, improving reproducibility and objectivity while enabling rapid analysis of large datasets—capabilities that are especially valuable in time-sensitive scenarios such as disaster victim identification, where hundreds of remains may need assessment under acute pressure. The multitask design means a single scan of a single bone yields both components of the biological profile at once. The authors argue that extending AI-based approaches to understudied skeletal elements could advance the field further, and they call for larger, more heterogeneous datasets, including multi-population cohorts and incomplete skeletal material, to validate and generalize the approach. If those efforts succeed, the humble thigh bone—already the bone most likely to survive environmental degradation—may become the backbone of a new generation of automated forensic identification, in which an algorithm reads the story written in bone faster, and perhaps more consistently, than any human expert.

Subject of Research: Deep learning–based estimation of age and sex from femoral postmortem CT images in a Japanese population

Article Title: Age and sex estimation from Japanese femoral computed tomographic images using foundation models

Article References: Torimitsu, S., Ikari, H., Tsuneya, S., Uemura, Y., Okada, M., Kojima, M., Al Mansoori, R., Iwase, H., Kawakami, E., & Makino, Y. (2026). Age and sex estimation from Japanese femoral computed tomographic images using foundation models. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-03998-5

Image Credits: AI Generated

DOI: 10.1007/s00414-026-03998-5

Keywords: forensic anthropology, deep learning, foundation models, postmortem CT, femur, sex estimation, age estimation, Vision Transformer, LoRA, multitask learning, biological profile, victim identification

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Tags: advances in skeletal biometricsage estimationAI in forensic identificationautomated skeletal analysisbiological profiledeep learningdeep learning for biological profilefemurfemur CT scans for age and sex estimationforensic anthropologyfoundation modelsfoundation models in medical imaginggender and age prediction from bonesJapanese forensic imaging researchLoRamultitask learningneural networks in forensic anthropologypostmortem CTpostmortem CT analysissex estimationtrauma and fragment analysis in forensic sciencevictim identificationvision transformer