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PubMed · 14653563

AI-guided parameter optimization in inverse treatment planning.

Hui Yan·Fang-Fang Yin·Huai-qun Guan·Jae Ho Kim
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Abstract

An artificial intelligence (AI)-guided inverse planning system was developed to optimize the combination of parameters in the objective function for intensity-modulated radiation therapy (IMRT). In this system, the empirical knowledge of inverse planning was formulated with fuzzy if-then rules, which then guide the parameter modification based on the on-line calculated dose. Three kinds of parameters (weighting factor, dose specification, and dose prescription) were automatically modified using the fuzzy inference system (FIS). The performance of the AI-guided inverse planning system (AIGIPS) was examined using the simulated and clinical examples. Preliminary results indicate that the expected dose distribution was automatically achieved using the AI-guided inverse planning system, with the complicated compromising between different parameters accomplished by the fuzzy inference technique. The AIGIPS provides a highly promising method to replace the current trial-and-error approach.

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AlgorithmsArtificial IntelligenceBody BurdenComputer SimulationFeasibility StudiesFuzzy LogicHumansModels, BiologicalQuality ControlRadiation ProtectionRadiometryRadiotherapy DosageRadiotherapy Planning, Computer-AssistedRadiotherapy, ConformalReproducibility of ResultsSensitivity and SpecificitySpinal NeoplasmsComparative StudyEvaluation StudyJournal Article

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Hui Yan, Fang-Fang Yin, Huai-qun Guan, Jae Ho Kim. 2003-11-07. AI-guided parameter optimization in inverse treatment planning.. https://doi.org/10.1088/0031-9155%2F48%2F21%2F008

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