Publications by authors named "Nur Amelia Bachtiar"
Article Synopsis
- Cranial sex estimation traditionally relies on visual assessments by forensic anthropologists, which can be biased and less accurate for diverse populations.
- This study investigates a deep learning (DL) framework to improve sex estimation accuracy using 200 CT scans of Indonesian individuals, finding that the top DL model achieved 97% accuracy, significantly higher than the human observer's 82%.
- The results suggest that DL models can effectively analyze cranial traits while considering overall size and shape, offering a valuable tool to enhance the reliability of sex estimation in forensic anthropology.
Ischemic stroke is the second leading cause of mortality and morbidity worldwide. Due to the urgency of implementing immediate therapy, acute stroke necessitates prompt diagnosis. The current gold standards for vascular imaging in stroke include computed tomography angiography (CTA), digital subtraction angiography (DSA) and magnetic resonance angiography (MRA).
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- Klales et al. (2012) is a widely used method for estimating skeletal sex, but its accuracy can improve when adjusted for specific populations, particularly with age affecting pelvic traits.
- This study analyzed pelvic CT scans from 378 individuals in Indonesia to evaluate the effectiveness of both Klales and Kenyhercz standards, finding lower classification accuracy than the original studies.
- It was determined that Indonesian-specific models, especially the ventral arc and a three-trait model, provided high accuracy rates, with the best results seen in age-dependent models for individuals aged 30-49 years.
Article Synopsis
- - Renewed interest in developing forensic anthropological standards in Asia focuses on creating population-specific models, particularly for the Indonesian population, which currently lacks dedicated morphoscopic studies.
- - The study evaluates the applicability of the Walker (2008) morphoscopic standards for estimating skeletal sex by analyzing 200 cranial MSCT scans from Indonesia using digital imaging.
- - Results indicate that the best univariate model achieved an 82.0% classification accuracy, while multiple trait assessments reached accuracies of 95.2% for females and 82.8% for males, providing a reliable framework for forensic practitioners in Indonesia.