{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:36:50Z","timestamp":1772138210000,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T00:00:00Z","timestamp":1732752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"European Union Horizon 2020 Research and Innovation"},{"name":"Marie Sk\u0142odowska-Curie","award":["754513"],"award-info":[{"award-number":["754513"]}]},{"DOI":"10.13039\/501100002739","name":"Aarhus University Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 24\u00a0384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width\/height, and isocenter position. Equivalent organ doses as well as DAP values were recorded. Next, using the aforementioned scan parameters as inputs, neural networks (NN) were trained using Keras for estimating the equivalent dose per DAP for each organ. Two methods were explored for positional input features: (1) \u201cCoordinate\u201d mode, which uses the (continuous) XYZ coordinates of the isocentre, and (2) \u201cAP\/JAW\u201d mode, which uses the (categorical) anteroposterior and craniocaudal position. Each network was trained, validated, and tested using a 3\/1\/1 data split. Effective dose (ED) was calculated from the combination of NN outputs using ICRP 103 tissue weighting factors. The performance of the resulting NN models for estimating ED\/DAP was compared with that of a multiple linear regression (MLR) model as well as direct conversion coefficients (CC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The mean absolute error (MAE) for organ dose\/DAP on the test data ranged from 0.18% (bone surface) to 2.90% (oesophagus) in \u201cCoordinate\u201d mode and from 2.74% (red bone marrow) to 14.13% (brain) in \u201cAP\/JAW\u201d mode. The MAE for ED was 0.23% and 4.30%, respectively, for the two modes, vs. 5.70% for the MLR model and 20.19%-32.67% for the CCs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>NNs allow for an accurate estimation of patient dose based on DAP in dental CBCT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/dmfr\/twae067","type":"journal-article","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T11:57:45Z","timestamp":1732795065000},"page":"188-202","source":"Crossref","is-referenced-by-count":1,"title":["Converting dose-area product to effective dose in dental cone-beam computed tomography using organ-specific deep learning"],"prefix":"10.1093","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9462-7546","authenticated-orcid":false,"given":"Ruben","family":"Pauwels","sequence":"first","affiliation":[{"name":"Department of Dentistry and Oral Health, Aarhus University , 8000 Aarhus,","place":["Denmark"]},{"name":"Aarhus Institute of Advanced Studies, Aarhus University , 8000 Aarhus,","place":["Denmark"]},{"name":"Department of Radiology, Faculty of Dentistry, Chulalongkorn University , 10330 Bangkok,","place":["Thailand"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,28]]},"reference":[{"issue":"1-4","key":"2025030423240522100_twae067-B1","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1093\/rpd\/ncv057","article-title":"Cone beam CT for dental and maxillofacial imaging: dose matters","volume":"165","author":"Pauwels","year":"2015","journal-title":"Radiat Prot Dosimetry"},{"issue":"4","key":"2025030423240522100_twae067-B2","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.adaj.2023.12.002","article-title":"Optimizing radiation safety in dentistry: clinical recommendations and regulatory considerations","volume":"155","author":"Benavides","year":"2024","journal-title":"J Am Dent Assoc"},{"key":"2025030423240522100_twae067-B3","first-page":"1","article-title":"The 2007 recommendations of the international commission on radiological protection. 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