Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities

The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, w...

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Bibliographic Details
Published in:Diagnostics (Basel) 2024-10, Vol.14 (20), p.2336
Main Authors: Mureșanu, Sorana, Hedeșiu, Mihaela, Iacob, Liviu, Eftimie, Radu, Olariu, Eliza, Dinu, Cristian, Jacobs, Reinhilde, On Behalf Of Team Project Group
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Cysts
Datasets
deep learning
Dental implants
Dentistry
Head & neck cancer
Health aspects
lesion detection
Medical care
Methods
Needs assessment
oral health
Orthodontics
panoramic radiography
Pathology
Pharmacy
Prostheses
Radiation therapy
Surgical apparatus & instruments
Technology application
Teeth
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Summary:The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy. A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices. The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability. YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14202336