The research, published in the International Journal of Legal Medicine, was led by Sushil Kumar Battan and colleagues at the Postgraduate Institute of Medical Education and Research in Chandigarh together with collaborators at Chandigarh University. The team carried out a retrospective cross-sectional analysis of computed tomography scans from 1,972 individuals aged between 18 and 80 years, all drawn from a northwestern Indian population. Rather than relying on the mean tissue depths that have dominated facial reconstruction practice for decades, the researchers measured both total craniofacial soft tissue thickness and the subcutaneous fat layer thickness separately, at 73 standardized craniofacial landmarks.

The distinction matters because the total thickness at any point on the face is not a single uniform material. It is a composite of skin, muscle, fascia, glands and fat, and only the subcutaneous fat component changes dramatically with age, sex, nutrition and body composition. Most existing reference tables, including large in vivo databases assembled for European populations, collapse all of these layers into a single number. By separating the adipose from the non-adipose contribution, the Indian dataset offers reconstructers a two-parameter description of facial architecture, which in principle allows far more nuanced modeling of how a face would have looked in life.

Technically, the measurements were extracted from CT imaging, which provides the millimeter-scale resolution needed to delineate the boundary between the bone, the deeper soft tissues and the subcutaneous fat plane. The statistical analysis combined classical tools with machine learning. Descriptive statistics established the baseline distributions at each landmark, independent t-tests quantified differences between the sexes, analysis of variance tested variation across age groups, and Pearson correlation assessed how tightly the two thickness measures track each other. On top of this, the team trained regression and machine learning algorithms to predict tissue thicknesses and to evaluate how well different variables explain the observed variation.

The results confirm that total soft tissue thickness consistently exceeds fat layer thickness at every one of the 73 landmarks, underscoring that a substantial portion of facial volume comes from muscle and other non-adipose structures. At the level of individual landmarks, the correlation between the two measures was strong, with a coefficient of determination of roughly 0.785, meaning that about three quarters of the landmark-level variation in total thickness can be accounted for by the fat layer alone. Interestingly, that relationship weakens dramatically when data are aggregated by age group, where the correlation drops to about 0.143. This suggests that while fat is a dominant driver of thickness at any given anatomical point, age-related changes in the two layers follow partially independent trajectories, likely reflecting muscle atrophy and connective tissue remodeling that proceed on different schedules from fat redistribution.

Sexual dimorphism emerged clearly in the data, with males showing higher values than females across the landmark set, although the effect sizes were small. That combination of statistical significance and modest magnitude is a familiar pattern in facial anthropometry and carries a practical warning for forensic practitioners: knowing the sex of a skull helps narrow the expected tissue depths, but it does not remove the need for population- and age-appropriate reference values. The age analysis revealed a classic increase-peak-decline pattern, with thickness measurements rising through early adulthood, reaching a maximum in middle adulthood and then declining in older age groups, mirroring the well-documented trajectory of facial fat compartments and the progressive resorption of the underlying facial skeleton.

One of the most striking findings concerns symmetry. When the researchers compared measurements taken on the right and left sides of the same faces, they found an extremely high correlation, with a coefficient of determination of 0.992. For forensic facial approximation, this is reassuring news, because it means that reference values collected on one side of the head can be applied to the other with negligible loss of accuracy, and that genuine bilateral asymmetry in soft tissue depth is rare enough to be treated as an exception rather than a rule in reconstruction work.

The artificial intelligence component of the study is where the project pushes furthest beyond conventional practice. The team compared machine learning models against standard linear regression and found that the best-performing algorithms, particularly a Random Forest model, achieved coefficients of determination exceeding 0.90 within the dataset, outperforming conventional approaches and showing limited systematic bias. Random Forest methods work by building an ensemble of decision trees, each trained on random subsets of the data and features, and averaging their predictions; this makes them well suited to capturing the nonlinear interactions between age, sex, landmark position and body composition that govern facial tissue thickness, interactions that a single linear equation cannot represent.

The authors are careful to flag the central limitation of these results: the models demonstrated strong within-dataset performance, but independent external validation is required before the predictions can be trusted on new populations or imaging systems. A model that achieves an R-squared above 0.90 on the data it was trained and tested on may still degrade when confronted with scans from a different scanner, a different ancestry group or a different body mass distribution. This caution reflects a broader lesson from the application of machine learning in medical imaging, where promising internal metrics have repeatedly failed to translate without rigorous external testing.

Even with that caveat, the study delivers something the forensic community has long needed: a large, CT-derived, population-specific reference database for a northwestern Indian population, one of the most populous regions of the world that has been underrepresented in facial reconstruction literature. Most established tissue depth tables were built from cadaver measurements or smaller imaging samples of European and East Asian subjects, and applying them to other populations introduces systematic error into reconstructed faces. By integrating total soft tissue thickness and fat layer thickness across 73 landmarks, quantifying sex and age effects, and demonstrating near-perfect bilateral symmetry, the Chandigarh team has produced a foundation for more accurate, data-driven facial approximation. As AI-guided craniofacial reconstruction systems mature, datasets of this kind will be the raw material that determines whether a reconstructed face becomes a recognizable likeness or just an anatomical guess.

Subject of Research: AI-based CT measurement of craniofacial soft tissue and fat thickness for forensic facial reconstruction in a northwestern Indian population

Article Title: Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population

Article References: Battan, S. K., Sharma, M., Garg, M., Singh, P., verma, P., & Singla, N. (2026). Artificial intelligence -based modeling and comparative evaluation of craniofacial soft tissue and subcutaneous fat thickness using ct imaging for forensic identification in a northwestern indian population. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-04000-y

Image Credits: AI Generated

DOI: 10.1007/s00414-026-04000-y

Keywords: forensic science, facial reconstruction, craniofacial soft tissue thickness, subcutaneous fat, computed tomography, artificial intelligence, machine learning, sexual dimorphism, aging, bilateral symmetry, forensic identification, population-specific database