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Data cleaning consumes about 80% of the time spent on data analysis forclinical research projects. This is a much bigger problem in the era of bigdata and machine learning in the field of medicine where large volumes of dataare being generated. We report an initial effort towards automated patient datacleaning using deep learning: the standardization of organ labeling inradiation therapy. Organs are often labeled inconsistently at differentinstitutions (sometimes even within the same institution) and at different timeperiods, which poses a problem for clinical research, especially formulti-institutional collaborative clinical research where the acquired patientdata is not being used effectively. We developed a convolutional neural network(CNN) to automatically identify each organ in the CT image and then label itwith the standardized nomenclature presented at AAPM Task Group 263. We testedthis model on the CT images of 54 patients with prostate and 100 patients withhead and neck cancer who previously received radiation therapy. The modelachieved 100% accuracy in detecting organs and assigning standardized labelsfor the patients tested. This work shows the feasibility of using deep learningin patient data cleaning that enables standardized datasets to be generated foreffective intra- and interinstitutional collaborative clinical research.

Towards automated patient data cleaning using deep learning: A feasibility study on the standardization of organ labeling