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Generalizability of Deep Learning Models for Dental Image Analysis by Krois, Joachim (author);Garcia Cantu, Anselmo (author);Chaurasia, Akhilanand (author);Patil, Ranjitkumar (author);Chaudhari, Prabhat Kumar (author);Gaudin, Robert (author);Gehrung, Sascha (author);Schwendicke, Falk (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

## Abstract We assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charité, Berlin, n = 650) and one in India (KGMU, Lucknow, n = 650): First, U-Net type models were trained on images from Charité (n = 500) and assessed on test sets from Charité and KGMU (each n = 150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charité images showed a (mean ± SD) F1-score of 54.1 ± 0.8% on Charité and 32.7 ± 0.8% on KGMU data (p < 0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images impr

Author

Krois, Joachim (author);Garcia Cantu, Anselmo (author);Chaurasia, Akhilanand (author);Patil, Ranjitkumar (author);Chaudhari, Prabhat Kumar (author);Gaudin, Robert (author);Gehrung, Sascha (author);Schwendicke, Falk (author)

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)