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Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning\u2013based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning\u2013based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4\u00a0px (8.2\u00a0mm) for<jats:italic>x<\/jats:italic>and 22.4\u00a0px (11.2\u00a0mm) for<jats:italic>y<\/jats:italic>. The evaluation of the temperature extraction revealed a mean absolute error of 0.55\u00a0<jats:inline-formula><jats:alternatives><jats:tex-math>$$^{\\circ }$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:msup><mml:mrow\/><mml:mo>\u2218<\/mml:mo><\/mml:msup><\/mml:math><\/jats:alternatives><\/jats:inline-formula>C. A final performance of 31\u00a0fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.<\/jats:p><\/jats:sec><jats:sec><jats:title>Graphical abstract<\/jats:title><\/jats:sec>","DOI":"10.1007\/s11517-022-02561-9","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T21:08:57Z","timestamp":1651612137000},"page":"1787-1800","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Camera fusion for real-time temperature monitoring of neonates using deep learning"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3842-6897","authenticated-orcid":false,"given":"Simon","family":"Lyra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6ran","family":"Rixen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad","family":"Heimann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srinivasa","family":"Karthik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayaraj","family":"Joseph","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumutha","family":"Jayaraman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thorsten","family":"Orlikowsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohanasankar","family":"Sivaprakasam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Leonhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Hoog Antink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"2561_CR1","doi-asserted-by":"publisher","unstructured":"Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, Lawn J, Cousens S, Black R (2016) Global, regional, and national causes of under-5 mortality in 2000\u201315: an updated systematic analysis with implications for the sustainable development goals. 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