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Embracing Computer Vision for Diagnostic Maxillofacial Imaging -- An Artificial Intelligence Machine Learning (AIML)Pilot Project.

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Title: Embracing Computer Vision for Diagnostic Maxillofacial Imaging -- An Artificial Intelligence Machine Learning (AIML)Pilot Project.
Authors: Akadiri, Oladimeji Adeniyi, Yarhere, Kesiena Seun
Source: Nigerian Medical Journal; 2025, Vol. 66 Issue 3, p973-982, 10p
Subject Terms: ARTIFICIAL intelligence, MACHINE learning, CLINICAL decision making, DIAGNOSIS, COMPUTER vision, JAWS, DIGITAL health, COMPUTER-assisted image analysis (Medicine)
Abstract: Background: Artificial intelligence (AI) is rapidly transforming healthcare, particularly in diagnostic medical imaging. For Nigerian Oral and Maxillofacial surgeons, embracing AI technologies is essential to improve diagnostic accuracy and maintain global relevance. This study aimed to demonstrate the potential of machine learning (ML) tools in enhancing diagnostic precision in maxillofacial radiology. Methodology: A supervised learning model was developed using Google's Teachable Machine, a no-code ML platform based on computer vision. Radiological images of histologically confirmed lesions were retrieved. Two projects were conducted: Project 1 trained the model to distinguish between malignant and benign bony jaw lesions using 46 radiographs (panoramic and sectional CT images). Project 2 trained the model to differentiate between craniofacial fibrous dysplasia and ossifying fibroma, using 40 radiographs. Each model was tested on five new images. The output probabilities were analyzed, and standard performance metrics--accuracy, precision, recall (sensitivity), and F1-score--were computed. Additionally, ROC-AUC (Receiver Operating Characteristic -- Area Under the Curve) curves were generated using Python code on Google Colaboratory IDE. Results: In Project 1, the model yielded predictive probabilities ranging from 89% to 100% for distinguishing malignant from benign lesions. In Project 2, it produced 71% to 100% probabilities for classifying fibrous dysplasia versus ossifying fibroma. Applying a 70% probability threshold for positive prediction, both models achieved perfect scores (1.0) across all performance metrics, including AUC = 1.00. Conclusion: AI-driven computer vision models show strong potential for improving diagnostic workflows in maxillofacial imaging. Their application can support more efficient clinical decision-making. However, the use of small test samples may have resulted in overfitting. Future studies with larger datasets and increased AI literacy among clinicians are essential for real-world implementation in resource-limited settings. [ABSTRACT FROM AUTHOR]
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Database: Biomedical Index
Description
Abstract:Background: Artificial intelligence (AI) is rapidly transforming healthcare, particularly in diagnostic medical imaging. For Nigerian Oral and Maxillofacial surgeons, embracing AI technologies is essential to improve diagnostic accuracy and maintain global relevance. This study aimed to demonstrate the potential of machine learning (ML) tools in enhancing diagnostic precision in maxillofacial radiology. Methodology: A supervised learning model was developed using Google's Teachable Machine, a no-code ML platform based on computer vision. Radiological images of histologically confirmed lesions were retrieved. Two projects were conducted: Project 1 trained the model to distinguish between malignant and benign bony jaw lesions using 46 radiographs (panoramic and sectional CT images). Project 2 trained the model to differentiate between craniofacial fibrous dysplasia and ossifying fibroma, using 40 radiographs. Each model was tested on five new images. The output probabilities were analyzed, and standard performance metrics--accuracy, precision, recall (sensitivity), and F1-score--were computed. Additionally, ROC-AUC (Receiver Operating Characteristic -- Area Under the Curve) curves were generated using Python code on Google Colaboratory IDE. Results: In Project 1, the model yielded predictive probabilities ranging from 89% to 100% for distinguishing malignant from benign lesions. In Project 2, it produced 71% to 100% probabilities for classifying fibrous dysplasia versus ossifying fibroma. Applying a 70% probability threshold for positive prediction, both models achieved perfect scores (1.0) across all performance metrics, including AUC = 1.00. Conclusion: AI-driven computer vision models show strong potential for improving diagnostic workflows in maxillofacial imaging. Their application can support more efficient clinical decision-making. However, the use of small test samples may have resulted in overfitting. Future studies with larger datasets and increased AI literacy among clinicians are essential for real-world implementation in resource-limited settings. [ABSTRACT FROM AUTHOR]
ISSN:03001652
DOI:10.71480/nmj.v66i3.748