Scholarlink

Background <p>Accurately determining sex using features like facial bone profiles and teeth is crucial for identifying unknown victims. Lateral cephalometric radiographs effectively depict the lateral cranial structure, aiding the development of computational identification models.</p> Objective <p>This study develops and evaluates a sex prediction model using cephalometric radiographs with several convolutional neural network (CNN) architectures. The primary goal is to evaluate the model’s performance on standardized radiographic data and real-world cranial photographs to simulate forensic applications.</p> Methods <p>Six CNN architectures—VGG16, VGG19, MobileNetV2, ResNet50V2, InceptionV3, and InceptionResNetV2—were employed to train and validate 340 cephalometric images of Indonesian individuals aged 18 to 40 years. The data were divided into training (70%), validation (15%), and testing (15%) subsets. Data augmentation was implemented to mitigate class imbalance. Additionally, a set of 40 cranial images from anatomical specimens was employed to evaluate the model’s generalizability. Model performance metrics included accuracy, precision, recall, and F1-score.</p> Results <p>CNN models were trained and evaluated on 340 cephalometric images (255 females and 85 males). VGG19 and ResNet50V2 achieved high F1-scores of 95% (females) and 83% (males), respectively, using cephalometric data, highlighting their strong class-specific performance. Although the overall accuracy exceeded 90%, the F1-score better reflected model performance in this imbalanced dataset. In contrast, performance notably decreased with cranial photographs, particularly when classifying female samples. That is, while InceptionResNetV2 achieved the highest F1-score for cranial photographs (62%), misclassification of females remained significant. Confusion matrices and per-class metrics further revealed persistent issues related to data imbalance and generalization across imaging modalities.</p> Conclusions <p>Basic CNN models perform well on standardized cephalometric images but less effectively on photographic cranial images, indicating a domain shift between image types that limits generalizability. Improving real-world forensic performance will require further optimization and more diverse training data.</p> Clinical trial number <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and evaluation of a convolutional neural network model for sex prediction using cephalometric radiographs and cranial photographs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

查看全文
  • Vitria Wuri Handayani,
  • Mieke Sylvia Margareth Amiatun Ruth,
  • Riries Rulaningtyas,
  • Muhammad Rasyad Caesarardhi,
  • Bayu Azra Yudhantorro,
  • Ahmad Yudianto

摘要

Background

Accurately determining sex using features like facial bone profiles and teeth is crucial for identifying unknown victims. Lateral cephalometric radiographs effectively depict the lateral cranial structure, aiding the development of computational identification models.

Objective

This study develops and evaluates a sex prediction model using cephalometric radiographs with several convolutional neural network (CNN) architectures. The primary goal is to evaluate the model’s performance on standardized radiographic data and real-world cranial photographs to simulate forensic applications.

Methods

Six CNN architectures—VGG16, VGG19, MobileNetV2, ResNet50V2, InceptionV3, and InceptionResNetV2—were employed to train and validate 340 cephalometric images of Indonesian individuals aged 18 to 40 years. The data were divided into training (70%), validation (15%), and testing (15%) subsets. Data augmentation was implemented to mitigate class imbalance. Additionally, a set of 40 cranial images from anatomical specimens was employed to evaluate the model’s generalizability. Model performance metrics included accuracy, precision, recall, and F1-score.

Results

CNN models were trained and evaluated on 340 cephalometric images (255 females and 85 males). VGG19 and ResNet50V2 achieved high F1-scores of 95% (females) and 83% (males), respectively, using cephalometric data, highlighting their strong class-specific performance. Although the overall accuracy exceeded 90%, the F1-score better reflected model performance in this imbalanced dataset. In contrast, performance notably decreased with cranial photographs, particularly when classifying female samples. That is, while InceptionResNetV2 achieved the highest F1-score for cranial photographs (62%), misclassification of females remained significant. Confusion matrices and per-class metrics further revealed persistent issues related to data imbalance and generalization across imaging modalities.

Conclusions

Basic CNN models perform well on standardized cephalometric images but less effectively on photographic cranial images, indicating a domain shift between image types that limits generalizability. Improving real-world forensic performance will require further optimization and more diverse training data.

Clinical trial number

Not applicable.