Artificial Intelligence in Facial Trauma, Oral Diseases, and Systemic Health | 29.24 MB
Title: Artificial Intelligence in Facial Trauma, Oral Diseases, and Systemic Health
Author: Tuan D. Pham
Category: Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Health & Well Being, Medical
Language: English | 359 Pages | ISBN: 9783032115317
Description:
This book explores the role of artificial intelligence in healthcare, focusing on cranio-maxillofacial trauma, oral health, and systemic disease. Part I establishes the foundations with core traditional machine learning and deep learning methods, imaging pipelines from classical features to CNNs and vision transformers, data augmentation, explainable AI, and sequential data approaches including RNNs, LSTMs, transformers, fuzzy recurrence plots, and scalable recurrence graph networks.
Parts II-IV highlight clinical applications. In facial trauma, chapters cover injury patterns and AI diagnostics as well as text-based mortality prediction and mandible network analysis. Surgical planning and simulation are addressed through 3D reconstruction, patient-specific implant design, outcome prediction, and workflow integration, with real-world examples in orthognathic surgery and fibula free flap reconstruction. Postoperative infection risk prediction is presented through multimodal monitoring. Oral health applications include AI for caries and periodontal disease, pediatric imaging enhanced by vision-language models, and cancer screening for early detection, biomarker discovery, and precision medicine. Oral-systemic links, including diabetes and cardiovascular disease, are analyzed using tensor models and recurrence-based methods.
Part V integrates trauma, oral health, and systemic conditions, and concludes with ethical, legal, and policy considerations, as well as future directions in federated learning, digital twins, and global health equity.
This book explores the role of artificial intelligence in healthcare, focusing on cranio-maxillofacial trauma, oral health, and systemic disease. Part I establishes the foundations with core traditional machine learning and deep learning methods, imaging pipelines from classical features to CNNs and vision transformers, data augmentation, explainable AI, and sequential data approaches including RNNs, LSTMs, transformers, fuzzy recurrence plots, and scalable recurrence graph networks.
Parts II-IV highlight clinical applications. In facial trauma, chapters cover injury patterns and AI diagnostics as well as text-based mortality prediction and mandible network analysis. Surgical planning and simulation are addressed through 3D reconstruction, patient-specific implant design, outcome prediction, and workflow integration, with real-world examples in orthognathic surgery and fibula free flap reconstruction. Postoperative infection risk prediction is presented through multimodal monitoring. Oral health applications include AI for caries and periodontal disease, pediatric imaging enhanced by vision-language models, and cancer screening for early detection, biomarker discovery, and precision medicine. Oral-systemic links, including diabetes and cardiovascular disease, are analyzed using tensor models and recurrence-based methods.
Part V integrates trauma, oral health, and systemic conditions, and concludes with ethical, legal, and policy considerations, as well as future directions in federated learning, digital twins, and global health equity.
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