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Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs.

Michael G Endres¹, Florian Hillen^1,2, Marios Salloumis³

¹Laboratory for Innovation Science, Harvard University, 175 N. Harvard Street, Suite 1350, Boston, MA 02134, USA.

Diagnostics (Basel, Switzerland)

|July 1, 2020

Summary

This summary is machine-generated.

Oral and maxillofacial (OMF) surgeons showed limitations in diagnosing periapical radiolucencies on panoramic radiographs. A developed deep learning algorithm demonstrated comparable or superior performance to many surgeons, offering potential diagnostic assistance.

Keywords:

artificial intelligence computer-assisted diagnosis image interpretation machine learning panoramic radiograph radiography

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Periapical radiolucencies are common dental findings with diverse causes.
Accurate detection on panoramic radiographs is crucial for diagnosis and treatment planning.

Purpose of the Study:

To evaluate the diagnostic performance of oral and maxillofacial (OMF) surgeons in identifying periapical radiolucencies on panoramic radiographs.
To compare surgeon performance against a novel deep learning algorithm.

Main Methods:

24 OMF surgeons assessed 2902 de-identified panoramic radiographs for periapical lucencies.
A predictive deep learning algorithm was developed and evaluated on the same dataset.
Diagnostic metrics including positive predictive value (PPV) and true positive rate (TPR) were calculated.

Main Results:

OMF surgeons had a mean PPV of 0.69 and TPR of 0.51.
The deep learning algorithm achieved a PPV of 0.67 and TPR of 0.51.
The algorithm outperformed 14 out of 24 OMF surgeons in diagnostic accuracy.

Conclusions:

Current diagnostic accuracy for periapical radiolucencies by OMF surgeons has limitations.
The developed deep learning algorithm shows promise as an assistive tool for OMF surgeons in detecting these radiographic findings.