Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis

Bibliographic Data

ID15498026
AuthorsDiego Raimondo (0000-0002-3235-4378, Azienda USL di Bologna), Antonio Raffone (0000-0001-5443-2333, University of Bologna, corresponding author), Anna Chiara Aru (0000-0003-4976-5912, University of Bologna), Matteo Giorgi (0000-0002-7515-5782, University of Siena), Ilaria Giaquinto (Ospedale G.B. Morgagni - L.Pierantoni), Emanuela Spagnolo (0000-0001-5566-8479, Hospital Universitario La Paz), Antonio Travaglino (0000-0003-4002-1618, University of Naples Federico II), Federico A Galatolo (0000-0001-7193-3754, University of Pisa), Mario G C A Cimino (0000-0002-1031-1959, University of Pisa), Jacopo Lenzi (0000-0003-2882-4223, University of Bologna), Gabriele Centini (0000-0002-6113-7401, University of Siena), Lucia Lazzeri (0000-0002-9513-1980, University of Siena), Antonio Mollo (0000-0002-6828-6389, University of Salerno), Renato Seracchioli (0000-0001-7487-1333, University of Bologna), Paolo Casadio (0000-0001-7706-5580, Azienda USL di Bologna)
Year2023
Volume20
Issue3
Pages1724-1724
Publication date2023-01-18
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph20031724
PMID36767092
OpenAlexW4317107488
LanguageEN
References cited26

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

Adenomyosis · Endometriosis · Gynecology · Radiology · Computer Science · Endometrial and Cervical Cancer Treatments · Endometriosis Research and Treatment · Medicine · Uterine Myomas and Treatments · Artificial Intelligence

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