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Published on: February 23, 2024

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

Eduardo Delamare^1,2, Xingyue Fu³, Zimo Huang³

¹Sydney Dental School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2050, Australia.

Dento Maxillo Facial Radiology

|January 26, 2024

Summary

This summary is machine-generated.

Managing imaging errors in panoramic radiography (PAN) datasets for machine learning (ML) models shows inconsistencies. Addressing these errors is crucial for reliable ML model development in dental imaging.

Keywords:

artificial intelligence dental panoramic radiograph imaging errors quality assessment systematic review

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Area of Science:

Radiology
Artificial Intelligence
Data Science

Background:

Panoramic radiography (PAN) is essential in dental diagnostics.
Machine learning (ML) models are increasingly used for analyzing medical images, including PAN datasets.
Image quality is a critical factor influencing the performance of ML models.

Conclusions:

Significant inconsistencies exist in managing PAN imaging errors for ML research.
Image quality issues negatively impact ML model performance.
Further research is needed to standardize image quality assessment and explore DL for automated quality control.