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Development and eva...
Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery
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- Boissin, Constance (författare)
- Karolinska Institutet
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- Laflamme, Lucie (författare)
- Karolinska Institutet
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- Fransén, Jian (författare)
- Uppsala universitet,Plastikkirurgi,Department of Plastic and Maxillofacial Surgery, Burn Center, Uppsala University Hospital, Uppsala, Sweden
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- Lundin, Mikael (författare)
- Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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- Huss, Fredrik, 1971- (författare)
- Uppsala universitet,Plastikkirurgi,Department of Plastic and Maxillofacial Surgery, Burn Center, Uppsala University Hospital, Uppsala, Sweden
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- Wallis, Lee (författare)
- Stellenbosch Univ, Fac Med & Hlth Sci, Div Emergency Med, Bellville, South Africa.;Univ Cape Town, Div Emergency Med, Cape Town, South Africa.
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- Allorto, Nikki (författare)
- Univ KwaZulu Natal, Dept Gen Surg, Pietermaritzburg Burn Serv, Pietermaritzburg, South Africa.
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- Lundin, Johan (författare)
- Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden.;Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.
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(creator_code:org_t)
- 2023-01-31
- 2023
- Engelska.
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kirurgi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Surgery (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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