Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis
Open accessAbstract
Background: This study aims to evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study were conducted between 1 and 30 April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound-skilled trainees and DL machine were asked to make a diagnosis reviewing uterine images. We evaluated and compared the accuracy, sensitivity, positive predictive value, F1-score, specificity and negative predictive value of the DL model and the trainees for adenomyosis diagnosis. Results: Accuracy of DL and intermediate ultrasound-skilled trainees for the diagnosis of adenomyosis were 0.51 (95% CI, 0.48–0.54) and 0.70 (95% CI, 0.60–0.79), respectively. Sensitivity, specificity and F1-score of DL were 0.43 (95% CI, 0.38–0.48), 0.82 (95% CI, 0.79–0.85) and 0.46 (0.42–0.50), respectively, whereas intermediate ultrasound-skilled trainees had sensitivity of 0.72 (95% CI, 0.52–0.86), specificity of 0.69 (95% CI, 0.58–0.79) and F1-score of 0.55 (95% CI, 0.43–0.66). Conclusions: In this preliminary study DL model showed a lower accuracy but a higher specificity in diagnosing adenomyosis on ultrasonographic images compared to intermediate-skilled trainees.
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In total, 11 articles were summarized in the other applications in gynecology section, including endometriosis, premature ovarian failure, uterine fibroids, follicle tracking, and ectopic pregnancies.
“In total, 11 articles were summarized in this section, including the topics of endometriosis [216,217], premature ovarian failure [218,219], uterine fibroids [220,221], follicle tracking [222,223], and ectopic pregnancies [224,225].”
The AI model of Raimondo et al. is useful to exclude rather than detect adenomyosis.
“interpreted as a useful tool to rather exclude than detect adenomyosis [217].”
Raimondo et al. showed a low sensitivity of the AI model to detect adenomyosis, but a high specificity.
“the results of Raimondo et al. showed a low sensitivity of the AI model to detect adenomyosis, but a high specificity”
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“In total, 11 articles were summarized in this section, including the topics of endometriosis [216,217], premature ovarian failure [218,219], uterine fibroids [220,221], follicle tracking [222,223], and ectopic pregnancies [224,225].”
BackgroundResult
- Authors
- Raimondo, D., Raffone, A., Aru, A. C., Giorgi, M., Giaquinto, I., Spagnolo, E., Travaglino, A., Galatolo, F. A., Cimino, M. G. C. A., Lenzi, J., Centini, G., Lazzeri, L., Mollo, A., Seracchioli, R., & Casadio, P.
- Affiliations
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- University of Siena (IT)
- Ospedale G.B. Morgagni - L.Pierantoni (IT)
- Hospital Universitario La Paz (ES)
- Agostino Gemelli University Polyclinic (IT)
- University of Pisa (IT)
- University of Bologna (IT)
- University of Salerno (IT)
- Corresponding author
- Raffone, Antonio
- ORCID
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2023-01-18
- Volume
- 20
- Issue
- 3
- Pages
- 1724
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC9914280
- OpenAlex
- W4317107488
- Cited by
- 44
- References
- 27
- Citations by year
- 2026: 14 · 2025: 11 · 2024: 11 · 2023: 8
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- IrInSubria (University of Insubria) (submittedVersion, cc-by)
- Use Siena air (University of Siena) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- adenomyosis, uterine adenomyosis, deep learning, transvaginal ultrasound, computer-aided diagnosis, diagnostic accuracy, uterine imaging
- MeSH terms
- Deep Learning, Endometriosis, Female, Humans, Sensitivity and Specificity, Ultrasonography, Uterus, Adenomyosis
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