Panoramic imaging errors in machine learning model development: a systematic review

Abstract

Objectives To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models. Methods This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature. Eligibility criteria PAN studies that used ML models and mentioned image quality concerns. Results Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias. Conclusions This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.

Oral Health Status and Treatment Needs Based on Artificial Intelligence (AI) Dental Panoramic Radiograph (DPR) Analysis: A Cross-Sectional Study

“…Imaging errors such as significantly overlapped structures, shadows of soft tissues or anatomical air spaces, and distortion may often be seen [50]. Such low-quality images may decrease algorithm performance if they are used in building machine learning models [4].…”

“…Moreover, a comprehensive analysis is time-consuming and vulnerable to bias due to the varying experiences of the evaluators [3]. High-quality radiographs are essential for accurate human diagnoses and for developing machine learning models that can assist dentists in their practice [4].…”