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

doi:10.3390/ijerph20031724

Observational Study

. 2023 Jan 18;20(3):1724.

doi: 10.3390/ijerph20031724.

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

Diego Raimondo¹, Antonio Raffone^{1

2}, Anna Chiara Aru^{1

2}, Matteo Giorgi³, Ilaria Giaquinto⁴, Emanuela Spagnolo⁵, Antonio Travaglino^{6

7}, Federico Andrea Galatolo⁸, Mario Giovanni Cosimo Antonio Cimino⁸, Jacopo Lenzi⁹, Gabriele Centini³, Lucia Lazzeri³, Antonio Mollo¹⁰, Renato Seracchioli^{1

2}, Paolo Casadio¹

Affiliations

¹ Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy.
² Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40126 Bologna, Italy.
³ Department of Molecular and Developmental Medicine, Obstetrics and Gynecological Clinic, University of Siena, 53100 Siena, Italy.
⁴ Department of Obstetrics and Gynecology, Morgagni-Pierantoni Hospital, 47100 Forlì, Italy.
⁵ Department of Obstetrics and Gynecology, Hospital Universitario La Paz, Paseo de la Castellana, 28046 Madrid, Spain.
⁶ Pathology Unit, Department of Woman and Child's Health and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.
⁷ Pathology Unit, Department of Advanced Biomedical Sciences, School of Medicine, University of Naples Federico II, 80138 Naples, Italy.
⁸ Department of Information Engineering, University of Pisa, 56100 Pisa, Italy.
⁹ Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
¹⁰ Gynecology and Obstetrics Unit, Department of Medicine, Surgery and Dentistry "Schola Medica Salernitana", University of Salerno, 84084 Baronissi, Italy.

PMID: 36767092
PMCID: PMC9914280
DOI: 10.3390/ijerph20031724

Observational Study

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

Diego Raimondo et al. Int J Environ Res Public Health. 2023.

. 2023 Jan 18;20(3):1724.

doi: 10.3390/ijerph20031724.

Authors

Affiliations

¹ Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy.
² Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40126 Bologna, Italy.
³ Department of Molecular and Developmental Medicine, Obstetrics and Gynecological Clinic, University of Siena, 53100 Siena, Italy.
⁴ Department of Obstetrics and Gynecology, Morgagni-Pierantoni Hospital, 47100 Forlì, Italy.
⁵ Department of Obstetrics and Gynecology, Hospital Universitario La Paz, Paseo de la Castellana, 28046 Madrid, Spain.
⁶ Pathology Unit, Department of Woman and Child's Health and Public Health Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.
⁷ Pathology Unit, Department of Advanced Biomedical Sciences, School of Medicine, University of Naples Federico II, 80138 Naples, Italy.
⁸ Department of Information Engineering, University of Pisa, 56100 Pisa, Italy.
⁹ Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
¹⁰ Gynecology and Obstetrics Unit, Department of Medicine, Surgery and Dentistry "Schola Medica Salernitana", University of Salerno, 84084 Baronissi, Italy.

PMID: 36767092
PMCID: PMC9914280
DOI: 10.3390/ijerph20031724

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: adenomyosis; artificial intelligence; deep learning; endometriosis; trainee; ultrasound.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

**Figure 1**
Confusion matrix of the DL diagnosis of the testing set.

See this image and copyright information in PMC

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References

1. Cunningham R.K., Horrow M.M., Smith R.J., Springer J. Adenomyosis: A Sonographic Diagnosis. RadioGraphics. 2018;38:1576–1589. doi: 10.1148/rg.2018180080. - DOI - PubMed
1. van den Bosch T., Dueholm M., Leone F.P.G., Valentin L., Rasmussen C.K., Votino A., Van Schoubroeck D., Landolfo C., Installé A.J., Guerriero S., et al. Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: A consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group. Ultrasound Obstet. Gynecol. 2015;46:284–298. doi: 10.1002/uog.14806. - DOI - PubMed
1. Van den Bosch T., de Bruijn A.M., de Leeuw R.A., Dueholm M., Exacoustos C., Valentin L., Bourne T., Timmerman D., Huirne J.A.F. Sonographic classification and reporting system for diagnosing adenomyosis. Ultrasound Obstet. Gynecol. 2019;53:576–582. doi: 10.1002/uog.19096. - DOI - PubMed
1. Exacoustos C., Morosetti G., Conway F., Camilli S., Martire F.G., Lazzeri L., Piccione E., Zupi E. New Sonographic Clas-sification of Adenomyosis: Do Type and Degree of Adenomyosis Correlate to Severity of Symptoms? J. Minim. Invasive Gynecol. 2020;27:1308–1315. doi: 10.1016/j.jmig.2019.09.788. - DOI - PubMed
1. Harada T., Khine Y.M., Kaponis A., Nikellis T., Decavalas G., Taniguchi F. The Impact of Adenomyosis on Women’s Fertility. Obstet. Gynecol. Survey. 2016;71:557–568. doi: 10.1097/OGX.0000000000000346. - DOI - PMC - PubMed

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[1] Cunningham R.K., Horrow M.M., Smith R.J., Springer J. Adenomyosis: A Sonographic Diagnosis. RadioGraphics. 2018;38:1576–1589. doi: 10.1148/rg.2018180080. - DOI - PubMed

[2] Cunningham R.K., Horrow M.M., Smith R.J., Springer J. Adenomyosis: A Sonographic Diagnosis. RadioGraphics. 2018;38:1576–1589. doi: 10.1148/rg.2018180080. - DOI - PubMed

[3] van den Bosch T., Dueholm M., Leone F.P.G., Valentin L., Rasmussen C.K., Votino A., Van Schoubroeck D., Landolfo C., Installé A.J., Guerriero S., et al. Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: A consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group. Ultrasound Obstet. Gynecol. 2015;46:284–298. doi: 10.1002/uog.14806. - DOI - PubMed

[4] van den Bosch T., Dueholm M., Leone F.P.G., Valentin L., Rasmussen C.K., Votino A., Van Schoubroeck D., Landolfo C., Installé A.J., Guerriero S., et al. Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: A consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group. Ultrasound Obstet. Gynecol. 2015;46:284–298. doi: 10.1002/uog.14806. - DOI - PubMed

[5] Van den Bosch T., de Bruijn A.M., de Leeuw R.A., Dueholm M., Exacoustos C., Valentin L., Bourne T., Timmerman D., Huirne J.A.F. Sonographic classification and reporting system for diagnosing adenomyosis. Ultrasound Obstet. Gynecol. 2019;53:576–582. doi: 10.1002/uog.19096. - DOI - PubMed

[6] Van den Bosch T., de Bruijn A.M., de Leeuw R.A., Dueholm M., Exacoustos C., Valentin L., Bourne T., Timmerman D., Huirne J.A.F. Sonographic classification and reporting system for diagnosing adenomyosis. Ultrasound Obstet. Gynecol. 2019;53:576–582. doi: 10.1002/uog.19096. - DOI - PubMed

[7] Exacoustos C., Morosetti G., Conway F., Camilli S., Martire F.G., Lazzeri L., Piccione E., Zupi E. New Sonographic Clas-sification of Adenomyosis: Do Type and Degree of Adenomyosis Correlate to Severity of Symptoms? J. Minim. Invasive Gynecol. 2020;27:1308–1315. doi: 10.1016/j.jmig.2019.09.788. - DOI - PubMed

[8] Exacoustos C., Morosetti G., Conway F., Camilli S., Martire F.G., Lazzeri L., Piccione E., Zupi E. New Sonographic Clas-sification of Adenomyosis: Do Type and Degree of Adenomyosis Correlate to Severity of Symptoms? J. Minim. Invasive Gynecol. 2020;27:1308–1315. doi: 10.1016/j.jmig.2019.09.788. - DOI - PubMed

[9] Harada T., Khine Y.M., Kaponis A., Nikellis T., Decavalas G., Taniguchi F. The Impact of Adenomyosis on Women’s Fertility. Obstet. Gynecol. Survey. 2016;71:557–568. doi: 10.1097/OGX.0000000000000346. - DOI - PMC - PubMed

[10] Harada T., Khine Y.M., Kaponis A., Nikellis T., Decavalas G., Taniguchi F. The Impact of Adenomyosis on Women’s Fertility. Obstet. Gynecol. Survey. 2016;71:557–568. doi: 10.1097/OGX.0000000000000346. - DOI - PMC - PubMed

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

Affiliations

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

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