Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models

doi:10.4329/wjr.123757

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World Journal of Radiology

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1949-8470

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Radiol. Aug 28, 2026; 18(8): 123757
Published online Aug 28, 2026. doi: 10.4329/wjr.123757

Table 4 Artificial intelligence model performance for architectural phenotype classification, 95%CI

Model	AUC	Accuracy (%)	Sensitivity (%)	Specificity (%)	Precision	F1-score
Logistic regression	0.92 (0.87-0.96)	85.0 (78.2-90.4)	87.2 (80.1-92.5)	82.6 (74.8-88.9)	0.84 (0.77-0.90)	0.84 (0.77-0.90)
Random forest	0.87 (0.81-0.92)	81.0 (73.7-86.9)	83.1 (75.4-89.1)	78.4 (70.2-85.1)	0.80 (0.72-0.86)	0.80 (0.72-0.86)
Support vector machine	0.84 (0.77-0.89)	79.0 (71.5-85.2)	80.4 (72.5-86.8)	76.8 (68.4-83.7)	0.78 (0.70-0.85)	0.78 (0.70-0.85)

AUC: Area under the curve.

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Citation: Sathish S, Nigam H, Gupta R. Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models. World J Radiol 2026; 18(8): 123757
URL: https://www.wjgnet.com/1949-8470/full/v18/i8/123757.htm
DOI: https://dx.doi.org/10.4329/wjr.123757

Sathish S, Nigam H, Gupta R. Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models. World J Radiol 2026; 18(8): 123757 [PMID: 42677009 DOI: 10.4329/wjr.123757]

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