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Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.

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Title: Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.
Authors: Li W; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China., Xia Y; Shuyuan Community Health Service Center, Pudong New District, Shanghai, 201304, China., Li Y; Mudanjiang Medical University, Heilongjiang, 157041, China., Wu X; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China. xing_apple@163.com., Shi L; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China. shilin_love@163.com.
Source: Journal of ovarian research [J Ovarian Res] 2026 Apr 15; Vol. 19 (1). Date of Electronic Publication: 2026 Apr 15.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101474849 Publication Model: Electronic Cited Medium: Internet ISSN: 1757-2215 (Electronic) Linking ISSN: 17572215 NLM ISO Abbreviation: J Ovarian Res Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.
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  Data: <searchLink fieldCode="AU" term="%22Li+W%22">Li W</searchLink>; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China.<br /><searchLink fieldCode="AU" term="%22Xia+Y%22">Xia Y</searchLink>; Shuyuan Community Health Service Center, Pudong New District, Shanghai, 201304, China.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; Mudanjiang Medical University, Heilongjiang, 157041, China.<br /><searchLink fieldCode="AU" term="%22Wu+X%22">Wu X</searchLink>; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China. xing_apple@163.com.<br /><searchLink fieldCode="AU" term="%22Shi+L%22">Shi L</searchLink>; Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai 6 Jiao Tong University School of Medicine, Shanghai Institute of Ultrasound in Medicine, Shanghai, 201306, China. shilin_love@163.com.
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  Data: <searchLink fieldCode="JN" term="%22101474849%22">Journal of ovarian research</searchLink> [J Ovarian Res] 2026 Apr 15; Vol. 19 (1). <i>Date of Electronic Publication: </i>2026 Apr 15.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central%22">BioMed Central </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101474849 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1757-2215 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2217572215%22">17572215 </searchLink><i>NLM ISO Abbreviation: </i>J Ovarian Res <i>Subsets: </i>MEDLINE
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      – Type: doi
        Value: 10.1186/s13048-026-02090-1
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      – Code: eng
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              Text: 2026 Apr 15
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