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• W4200451849 endingPage "100886" @default.
• W4200451849 startingPage "100886" @default.
• W4200451849 abstract "PurposeThe aim was to develop a novel artificial intelligence (AI)–guided clinical decision support system, to predict radiation doses to subsites of the mandible using diagnostic computed tomography scans acquired before any planning of head and neck radiation therapy (RT).Methods and MaterialsA dose classifier was trained using RT plans from 86 patients with oropharyngeal cancer; the test set consisted of an additional 20 plans. The classifier was trained to predict whether mandible subsites would receive a mean dose >50 Gy. The AI predictions were prospectively evaluated and compared with those of a specialist head and neck radiation oncologist for 9 patients. Positive predictive value (PPV), negative predictive value (NPV), Pearson correlation coefficient, and Lin concordance correlation coefficient were calculated to compare the AI predictions to those of the physician.ResultsIn the test data set, the AI predictions had a PPV of 0.95 and NPV of 0.88. For 9 patients evaluated prospectively, there was a strong correlation between the predictions of the AI algorithm and physician (P = .72, P < .001). Comparing the AI algorithm versus the physician, the PPVs were 0.82 versus 0.25, and the NPVs were 0.94 versus 1.0, respectively. Concordance between physician estimates and final planned doses was 0.62; this was 0.71 between AI-based estimates and final planned doses.ConclusionAI-guided decision support increased precision and accuracy of pre-RT dental dose estimates." @default.
• W4200451849 created "2021-12-31" @default.
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• W4200451849 date "2022-03-01" @default.
• W4200451849 modified "2023-10-14" @default.
• W4200451849 title "Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation" @default.
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• W4200451849 doi "https://doi.org/10.1016/j.adro.2021.100886" @default.
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