ADVANCED TECHNOLOGIES IN ORAL AND MAXILLOFACIAL SURGERY

MaxillofacialSurgerydakapak

ARTIFICIAL INTELLIGENCE (AI)DRIVEN DIAGNOSTIC TOOLS AND TREATMENT PLANNING IN ORAL AND MAXILLOFACIAL SURGERY

Ömer Uranbey

Aydın Adnan Menderes University, Faculty of Dentistry, Department of Oral and Maxillofacial Surgery, Aydın, Türkiye

Uranbey Ö. Artificial Intelligence (AI) Driven Diagnostic Tools and Treatment Planning in Oral and Maxillofacial Surgery. Karasu HA, ed. Advanced Technologies in Oral and Maxillofacial Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.101110.

ABSTRACT

Artificial intelligence (AI) is revolutionizing surgery by enhancing precision, efficiency, and patient outcomes through the use of advanced computing technologies that mimic human intelligence. In particular, machine learning (ML) and deep learning (DL) are transforming clinical practices, enabling surgeons to focus on patient care while automating complex tasks such as image analysis and surgical planning. Recent research highlights a significant increase in AI applications in Oral and Maxillofacial Surgery (OMFS), especially in orthognathic surgery, oral cancer detection, and diagnosis. ML, a sub set of AI, enables computers to learn patterns and make decisions without explicit programming. DL, utilizing multilayered neural networks, is highly effective in medical imaging analysis. AI employs supervised learning, where models learn from labeled datasets to predict outcomes such as fracture detection in CT scans. In contrast, unsupervised learning identifies patterns in unlabeled data, aiding in clustering similar cases and detecting anomalies in patient records. AIdriven imaging techniques improve diagnostic accuracy in OMFS. AIpowered algorithms enhance cone beam computed tomog raphy (CBCT) image quality by reducing motion artifacts, ensuring clearer imaging, and minimizing repeat scans. Automatic segmentation tools, like the nnUNet framework, facilitate precise identifica tion of anatomical structures, streamlining surgical planning. DL models, such as convolutional neural networks (CNNs), have demonstrated superior accuracy in detecting oral lesions, tumors, and frac tures, significantly improving early diagnosis and patient outcomes. In summary, AI holds immense untapped potential to advance the field of OMFS across various dimensions. This research could act as a contemporary guide for OMFS surgeons interested in AI, helping to integrate AI technologies to enhance surgical outcomes and patient care within the field.

Keywords: Artificial intelligence; Supervised machine learning; Unsupervised machine learning; Deep learning; Surgery; Oral; Natural language processing; Image processing computerassisted; Diagnostic imaging

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