{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T06:38:03Z","timestamp":1786430283223,"version":"build-2736575974"},"reference-count":21,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Skin Research and Technology"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi\u2010network fusion object detection framework based on the object detection technique Faster R\u2010CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi\u2010network fusion object detection framework was compared with that between dermatologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi\u2010network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi\u2010network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/srt.13698","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T01:54:15Z","timestamp":1713405255000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi\u2010Network Fusion Object Detection Framework for dermoscopic pattern diagnosis"],"prefix":"10.1111","volume":"30","author":[{"given":"Honghai","family":"Ji","sequence":"first","affiliation":[{"name":"School of Electronics &amp; Control Engineering North China University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4642-7371","authenticated-orcid":false,"given":"Jiaqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics &amp; Control Engineering North China University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Dermatology 72nd Group army hospital of PLA  Huzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Automation Beijing Information Science and Technology University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Dermatology 72nd Group army hospital of PLA  Huzhou China"},{"name":"Department of Dermatology Shanghai Key Laboratory of Medical Mycology Changzheng Hospital Naval Medical University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Dermatology 72nd Group army hospital of PLA  Huzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1159\/000018026"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1346-8138.2011.01366.x"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.17354"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdv.15344"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2133.2010.10180.x"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21082852"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2020.11.034"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/lsm.23324"},{"key":"e_1_2_9_10_1","unstructured":"RoyM ProtityA.Hair and Scalp Disease Detection using Machine Learning and Image Processing.2022."},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.965423"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8100283"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"SaraswathiC PushpaB.FRCNN based Deep Learning for Identification and Classification of Alopecia Areata. 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