Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

Priyanshu Kumar Shrivastava; Shamimul Hasan; Laraib Abid; Ranjit Injety; Ayush Kumar Shrivastav; Deborah Sybil

doi:10.1007/s11282-024-00745-7

Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis

Oral Radiol. 2024 Mar 26. doi: 10.1007/s11282-024-00745-7. Online ahead of print.

Authors

Priyanshu Kumar Shrivastava¹, Shamimul Hasan², Laraib Abid¹, Ranjit Injety³, Ayush Kumar Shrivastav⁴, Deborah Sybil⁵

Affiliations

¹ Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
² Department of Oral Medicine and Radiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
³ Department of Neurology, Christian Medical College & Hospital, Ludhiana, Punjab, India.
⁴ Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
⁵ Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India. dsybilg@gmail.com.

PMID: 38530559
DOI: 10.1007/s11282-024-00745-7

Abstract

Background: The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors.

Methods: A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT.

Results: 16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier.

Conclusion: The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.

Keywords: Artificial intelligence; Machine learning; Odontogenic cysts; Odontogenic tumors; Radiomics.

Publication types

Review