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

Diego Raimondo, Antonio Raffone (), Anna Chiara Aru, Matteo Giorgi, Ilaria Giaquinto, Emanuela Spagnolo, Antonio Travaglino, Federico Andrea Galatolo, Mario Giovanni Cosimo Antonio Cimino, Jacopo Lenzi, Gabriele Centini, Lucia Lazzeri, Antonio Mollo, Renato Seracchioli and Paolo Casadio
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Diego Raimondo: Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
Antonio Raffone: Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
Anna Chiara Aru: Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
Matteo Giorgi: Department of Molecular and Developmental Medicine, Obstetrics and Gynecological Clinic, University of Siena, 53100 Siena, Italy
Ilaria Giaquinto: Department of Obstetrics and Gynecology, Morgagni–Pierantoni Hospital, 47100 Forlì, Italy
Emanuela Spagnolo: Department of Obstetrics and Gynecology, Hospital Universitario La Paz, Paseo de la Castellana, 28046 Madrid, Spain
Antonio Travaglino: Pathology Unit, Department of Woman and Child’s Health and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Federico Andrea Galatolo: Department of Information Engineering, University of Pisa, 56100 Pisa, Italy
Mario Giovanni Cosimo Antonio Cimino: Department of Information Engineering, University of Pisa, 56100 Pisa, Italy
Jacopo Lenzi: Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy
Gabriele Centini: Department of Molecular and Developmental Medicine, Obstetrics and Gynecological Clinic, University of Siena, 53100 Siena, Italy
Lucia Lazzeri: Department of Molecular and Developmental Medicine, Obstetrics and Gynecological Clinic, University of Siena, 53100 Siena, Italy
Antonio Mollo: Gynecology and Obstetrics Unit, Department of Medicine, Surgery and Dentistry “Schola Medica Salernitana”, University of Salerno, 84084 Baronissi, Italy
Renato Seracchioli: Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
Paolo Casadio: Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy

IJERPH, 2023, vol. 20, issue 3, 1-9

Abstract: 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.

Keywords: artificial intelligence; deep learning; adenomyosis; endometriosis; trainee; ultrasound (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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