Digital removal of dermal denticle layer using geometric AI from 3D CT scans of shark craniofacial structures enhances anatomical precision.
Authors
Affiliations (13)
Affiliations (13)
- College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University, Chuncheon, Gangwon, Republic of Korea. [email protected].
- 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.
- School of Medical and Health Sciences, Tung Wah College, Homantin, Kowloon, Hong Kong Special Administrative Region, China.
- Department of Mechanical Engineering, Hanyang University, Seoul, Republic of Korea.
- Voronoi Diagram Research Center, Hanyang University, Seoul, Republic of Korea.
- College of Veterinary Medicine and Veterinary Medical Research Institute, Jeju National University, Jeju, Republic of Korea.
- Cetacean Research Institute, National Institute of Fisheries Science (CRI, NIFS), Ulsan, Republic of Korea.
- Department of Surgery, Queen Mary Hospital, Pokfulam, Hong Kong Special Administrative Region, China.
- Laboratory of Phage and Microbial Resistance, Department of Biological Sciences, Kyonggi University, Suwon, Republic of Korea.
- Laboratory of Veterinary Public Health, College of Veterinary Medicine, Jeonbuk National University, Jeonju, Jeollabuk-do, Republic of Korea.
- Department of Marine Industrial and Maritime Police, College of Ocean Science, Jeju National University, Jeju, Republic of Korea. [email protected].
- College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul National University, Seoul, Republic of Korea. [email protected].
Abstract
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.