Artificial Intelligence Versus Stepwise Regression for Stature Estimation from Tibial Dimensions: A Forensic Osteometric Study in South Sumatran Malay Adults

2026-04-29T07:15:55+00:00

Introduction: Stature estimation from skeletal elements constitutes a foundational component of forensic biological profiling, critically supporting disaster victim identification in disaster-prone nations such as Indonesia. Traditional Stepwise Multiple Linear Regression (SMLR), while widely employed, is constrained by linearity assumptions that inadequately model the complex, multidimensional osteometric biology of population-specific cohorts.

Methods: This cross-sectional study enrolled 450 healthy adult South Sumatran Malay participants (225 males, 225 females), aged 20–50 years, from Palembang and surrounding regencies. Five percutaneous tibial measurements were acquired under standardized protocols by a single trained anthropologist. A 70:30 stratified train-test split yielded 315 training and 135 test observations. Predictive performance of SMLR was rigorously compared against an optimized three-hidden-layer Multilayer Perceptron Artificial Neural Network (MLP-ANN).

Result: Significant sexual dimorphism was demonstrated across all variables (independent samples t-test, p < 0.001). Percutaneous Tibial Length (PTL) was the strongest individual stature predictor (males: r = 0.812; females: r = 0.795). The best SMLR pooled model (PTL + PDB + DDB) achieved R-squared = 0.742 and RMSE = ±4.82 cm. The MLP-ANN substantially outperformed SMLR across all subgroups, achieving a pooled R-squared of 0.914 and RMSE of ±2.78 cm-representing a 23.2% improvement in R-squared and a 42.3% reduction in prediction error.

Conclusion: These population-specific AI-driven standards offer forensic practitioners in the Indonesian medicolegal context a markedly more reliable tool for biological profiling of incomplete human remains.

Procedural Failures as Independent Predictors of Delayed Intracranial Foreign Body Diagnosis in Mild Traumatic Brain Injury: A Multivariable Logistic Regression Analysis from North Sumatra, Indonesia

2026-04-30T01:48:57+00:00

Introduction: Retained intracranial foreign bodies in patients presenting with a Glasgow Coma Scale (GCS) score of 15 represent a rare but potentially fatal diagnostic pitfall in emergency trauma management, carrying profound medicolegal consequences under Indonesian health legislation. This retrospective multi-centre cohort study identified independent procedural risk factors predicting delayed diagnosis (>24 hours) of intracranial foreign bodies among mild traumatic brain injury (mTBI) patients in North Sumatra, Indonesia.

Methods: Medical records from 1,240 mTBI patients (GCS 13–15) presenting to three tertiary trauma centres were reviewed; 45 cases with confirmed intracranial foreign bodies were identified, of whom 14 (31.1%) experienced delayed diagnosis.

Results: Bivariate analyses demonstrated that failure to perform digital wound palpation (78.6% vs. 9.7%, p<0.001) and non-adherence to neuroimaging guidelines (71.4% vs. 9.7%, p<0.001) were significantly overrepresented in the delayed group. Multivariable logistic regression, adjusted for age, sex, mechanism of injury, and centre, identified non-adherence to computed tomography guidelines (adjusted odds ratio [aOR] 8.71, 95% CI 3.80–21.45, p<0.001), failure to palpate the wound bed (aOR 6.42, 95% CI 2.15–18.50, p<0.001), and an initial GCS of 15 (aOR 3.15, 95% CI 1.42–7.21, p=0.012) as significant independent predictors. Model calibration was excellent (Hosmer–Lemeshow p=0.711; accuracy 88.4%).

Conclusion: A sentinel autopsy case demonstrated the fatal sequence: an undetected stone caused subarachnoid haemorrhage, brainstem compression, and asphyxial death. Strict protocol enforcement for secondary physical survey completion and CT guideline adherence is imperative to prevent fatal outcomes and mitigate medicolegal liability in Indonesian emergency departments.

Sriwijaya Journal of Forensic and Medicolegal

Artificial Intelligence Versus Stepwise Regression for Stature Estimation from Tibial Dimensions: A Forensic Osteometric Study in South Sumatran Malay Adults

Procedural Failures as Independent Predictors of Delayed Intracranial Foreign Body Diagnosis in Mild Traumatic Brain Injury: A Multivariable Logistic Regression Analysis from North Sumatra, Indonesia