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The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02277-2","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T09:01:59Z","timestamp":1693818119000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study"],"prefix":"10.1186","volume":"23","author":[{"given":"Guoqiu","family":"Li","sequence":"first","affiliation":[]},{"given":"Hongtian","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Huaiyu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Zhibin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Keen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuwei","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Siyuan","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Jinfeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Fajin","family":"Dong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"issue":"3","key":"2277_CR1","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1007\/s00330-020-07168-y","volume":"31","author":"KW Park","year":"2021","unstructured":"Park KW, Park S, Shon I, Kim M-J, Han B-k, Ko EY, Ko ES, Shin JH. 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The need for informed consent was waived by Institutional Review Board of the Shenzhen People\u2019s Hospital.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This retrospective study was approved by the Institutional Review Board of the Shenzhen People\u2019s Hospital. The need for informed consent was waived by Institutional Review Board of the Shenzhen People\u2019s Hospital. All methods were carried out in accordance with relevant guidelines and regulations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors state that this work has not received any funding.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}}],"article-number":"174"}}