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Raimondo D, Raffone A, Salucci P, Raimondo I, Capobianco G, Galatolo FA, Cimino MGCA, Travaglino A, Maletta M, Ferla S, Virgilio A, Neola D, Casadio P, Seracchioli R. Detection and Classification of Hysteroscopic Images Using Deep Learning. Cancers (Basel) 2024; 16:1315. [PMID: 38610993 PMCID: PMC11011142 DOI: 10.3390/cancers16071315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. AIM To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. METHODS A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. RESULTS We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. CONCLUSION Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
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Affiliation(s)
- Diego Raimondo
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
| | - Antonio Raffone
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
- Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, 80131 Naples, Italy;
| | - Paolo Salucci
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Ivano Raimondo
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
- Gynecology and Breast Care Center, Mater Olbia Hospital, 07026 Olbia, Italy
| | - Giampiero Capobianco
- Gynecologic and Obstetric Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Federico Andrea Galatolo
- Department of Information Engineering, University of Pisa, 56100 Pisa, Italy; (F.A.G.); (M.G.C.A.C.)
| | | | - Antonio Travaglino
- Unit of Pathology, Department of Medicine and Technological Innovation, University of Insubria, 21100 Varese, Italy;
| | - Manuela Maletta
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Stefano Ferla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Agnese Virgilio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Daniele Neola
- Department of Neuroscience, Reproductive Sciences and Dentistry, School of Medicine, University of Naples Federico II, 80131 Naples, Italy;
| | - Paolo Casadio
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
| | - Renato Seracchioli
- Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy; (D.R.); (P.C.); (R.S.)
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40127 Bologna, Italy; (M.M.); (S.F.)
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Raimondo D, Raffone A, Aru AC, Giorgi M, Giaquinto I, Spagnolo E, Travaglino A, Galatolo FA, Cimino MGCA, Lenzi J, Centini G, Lazzeri L, Mollo A, Seracchioli R, Casadio P. Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis. Int J Environ Res Public Health 2023; 20:ijerph20031724. [PMID: 36767092 PMCID: PMC9914280 DOI: 10.3390/ijerph20031724] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 06/01/2023]
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.
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Affiliation(s)
- 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
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40126 Bologna, Italy
| | - Anna Chiara Aru
- Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of 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
- Pathology Unit, Department of Advanced Biomedical Sciences, School of Medicine, University of Naples Federico II, 80138 Naples, 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
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40126 Bologna, Italy
| | - Paolo Casadio
- Division of Gynecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
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