{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:01:52Z","timestamp":1760151712207,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004704","name":"NATIONAL RESEARCH COUNCIL OF THAILAND","doi-asserted-by":"publisher","award":["N72B640122"],"award-info":[{"award-number":["N72B640122"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, \u2018pseudopatients\u2019 refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) of 1.2\u20131.6%, 0.65\u20130.8%, and 0.97\u20130.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.<\/jats:p>","DOI":"10.3390\/s22082918","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T22:48:45Z","timestamp":1649803725000},"page":"2918","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility"],"prefix":"10.3390","volume":"22","author":[{"given":"Utumporn","family":"Puangragsa","sequence":"first","affiliation":[{"name":"Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiraporn","family":"Setakornnukul","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pittaya","family":"Dankulchai","sequence":"additional","affiliation":[{"name":"Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2387-6059","authenticated-orcid":false,"given":"Pattarapong","family":"Phasukkit","sequence":"additional","affiliation":[{"name":"School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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