Kim Sang Wha, Yuen Adams Hei Long, Kim Hyun Woo, Lee Seyoung, Lee Sung Bin, Lee Young Min, Jung Won Joon, Poon Cherry Tsz Ching, Park Dasol, Kim Sangyun, Kim Sang Guen, Kang Jung Woo, Kwon Jun, Jo Su Jin, Giri Sib Sankar, Park Hyunjung, Seo Jong-Pil, Kim Deok-Soo, Kim Byung Yeop, Park Se Chang
College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University, Chuncheon, Gangwon, Republic of Korea.
College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea.
Sci Rep. 2025 Jun 4;15(1):19659. doi: 10.1038/s41598-025-04442-1.
Craniofacial morphometrics in sharks provide crucial insights into evolutionary history, geographical variation, sexual dimorphism, and developmental patterns. However, the fragile cartilaginous nature of shark craniofacial skeleton poses significant challenges for traditional specimen preparation, often resulting in damaged cranial landmarks and compromised measurement accuracy. While computed tomography (CT) offers a non-invasive alternative for anatomical observation, the high electron density of dermal denticles in sharks creates a unique challenge, obstructing clear visualization of internal structures in three-dimensional volume-rendered images (3DVRI). This study presents an artificial intelligence (AI)-based solution using machine-learning algorithms for digitally removing dermal denticle layer from CT scans of shark craniofacial skeleton. We developed a geometric AI-driven software (SKINPEELER) that selectively removes high-intensity voxels corresponding to dermal denticle layer while preserving underlying anatomical structures. We evaluated this approach using CT scans from 20 sharks (16 Carcharhinus brachyurus, 2 Alopias vulpinus, 1 Sphyrna lewini, and 1 Prionace glauca), applying our AI-driven software to process the Digital Imaging and Communications in Medicine (DICOM) images. The processed scans were reconstructed using bone reconstruction algorithms to enable precise craniofacial measurements. We assessed the accuracy of our method by comparing measurements from the processed 3DVRIs with traditional manual measurements. The AI-assisted approach demonstrated high accuracy (86.16-98.52%) relative to manual measurements. Additionally, we evaluated reproducibility and repeatability using intraclass correlation coefficients (ICC), finding high reproducibility (ICC: 0.456-0.998) and repeatability (ICC: 0.985-1.000 for operator 1 and 0.882-0.999 for operator 2). Our results indicate that this AI-enhanced digital denticle removal technique, combined with 3D CT reconstruction, provides a reliable and non-destructive alternative to traditional specimen preparation methods for investigating shark craniofacial morphology. This novel approach enhances measurement precision while preserving specimen integrity, potentially advancing various aspects of shark research including evolutionary studies, conservation efforts, and anatomical investigations.
鲨鱼的颅面形态测量学为了解其进化历史、地理变异、两性异形和发育模式提供了至关重要的见解。然而，鲨鱼颅面骨骼脆弱的软骨性质给传统标本制备带来了重大挑战，常常导致颅骨标志受损和测量准确性受影响。虽然计算机断层扫描（CT）为解剖观察提供了一种非侵入性的替代方法，但鲨鱼皮齿的高电子密度带来了独特的挑战，阻碍了三维容积渲染图像（3DVRI）中内部结构的清晰可视化。本研究提出了一种基于人工智能（AI）的解决方案，使用机器学习算法从鲨鱼颅面骨骼的CT扫描中数字去除皮齿层。我们开发了一种几何人工智能驱动的软件（SKINPEELER），该软件可以选择性地去除与皮齿层对应的高强度体素，同时保留底层的解剖结构。我们使用来自20条鲨鱼（16条短尾真鲨、2条狐形长尾鲨、1条路氏双髻鲨和1条大青鲨）的CT扫描来评估这种方法，应用我们的人工智能驱动软件处理医学数字成像和通信（DICOM）图像。使用骨重建算法对处理后的扫描进行重建，以实现精确的颅面测量。我们通过将处理后的3DVRIs测量结果与传统手动测量结果进行比较来评估我们方法的准确性。与手动测量相比，人工智能辅助方法显示出较高的准确性（86.16 - 98.52%）。此外，我们使用组内相关系数（ICC）评估了可重复性和重复性，发现具有高可重复性（ICC：0.456 - 0.998）和重复性（操作员1的ICC：0.985 - 1.000，操作员2的ICC：0.882 - 0.999）。我们的结果表明，这种人工智能增强的数字皮齿去除技术，结合三维CT重建，为研究鲨鱼颅面形态提供了一种可靠且无损的传统标本制备方法的替代方法。这种新方法提高了测量精度，同时保留了标本完整性，有可能推动鲨鱼研究的各个方面，包括进化研究、保护工作和解剖学研究。
