Objective: Dose distribution index (DDI) is a dose-volume parameter used in the treatment planning evaluation. DDI provides the dosimetric estimates on the target coverage, sparings of all organs-at-risk and remaining healthy tissue in the treated organ in a single parameter. In this study, the DDI value was predicted by machine learning model using different algorithms. 

Methods: The DDI were calculated using its original formula by definition. On the other hand, machine training was carried out to determine the DDI using the same data of 50 prostate volumetric modulated arc therapy (VMAT) plans from the Grand River Regional Cancer Centre, Kitchener, Ontario. Machine learning algorithms such as linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR) were used to predict the DDI value for each prostate VMAT treatment plan. For comparing the performance of the machine learning algorithms, root mean square error (RMSE), prediction time of the machine learning and training time were determined and compared.

Results: Comparing the RMSE values among all algorithms, only the DDI predicted by the medium and coarse tree regression algorithms showed a relatively large RMSE values in the range of 0.021 – 0.034. For other algorithms such as SVM and GRP, they all performed very well in predicting the DDI with smaller RMSE values ranging from 0.0038 to 0.0193. By considering other factors such as prediction speed and training time, the square exponential GPR algorithm had the smallest RMSE value of 0.0038, a relatively high prediction speed of 4,100 observation per second and a short machine training time of 0.18 second.

Conclusion: It is concluded that the family of GPR algorithms performed best in the dose distribution index prediction. It is expected that the accuracy of DDI prediction will increase with more plan data trained using such algorithm.

Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

Objective: We built a RT Bot, a chatbot with characterization for the patient, general public and radiation staff to provide educational information regarding radiotherapy using the artificial intelligence. The Bot was personalized by machine learning to detect the user’s temperament and intent in order to provide the best guidance to the user with a human-like response. 

Methods: The Bot was developed using the IBM Watson Assistant functionalities on the IBM cloud. Dataset of information was prepared for different user groups such as descriptions of all processes in radiotherapy, promotion of cancer screening especially high fatality and popular cancer sites, and basic cancer preventive measures such as how to maintain healthy life with suitable diet and exercise. To ensure correct information can be understood and digested by the users with their background (patient, general public and radiation staff), the Bot character was personalized through the IBM Watson Assistant functionalities such as natural language understanding, entities and slots.

Results: The Bot can be operated in a front-end window on any Internet-of-things such as smartphone, tablet, laptop and desktop. In the beginning, the Bot will communicate with the user intentionally with an introduction. The user can then type in any text to answer concerning their enquiry. The Bot usually begins by answering simple questions regarding radiotherapy and providing related information. If the Bot cannot understand the user’s wording, it will provide a guidance to help the user. 

Conclusion: A chatbot was built for interdisciplinary educational purpose for the patient, general public and radiation staff using artificial intelligence and machine learning. The Bot may be used by a cancer centres or some private sectors such as high school, community centres, volunteer group and charities, which promote cancer preventive measures and screening for a healthy life or educate user what is cancer and radiotherapy.

An AI-assisted Chatbot for Education in Radiotherapy

<i>**Session Chair: ** Cornelia Hoehr</i>

TS-6-2 CAP-COMP Medical Physics / Physique médicale ACP-OCPM

technical paper

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James Chow

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Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

An AI-assisted Chatbot for Education in Radiotherapy

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