Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: Comparison with expert subjective assessment
Open accessAbstract
OBJECTIVES: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective assessment (SA) by an ultrasound expert. METHODS: We included 3077 (grayscale, n = 1927; power Doppler, n = 1150) ultrasound images from 758 women with ovarian tumors, who were classified prospectively by expert ultrasound examiners according to IOTA (International Ovarian Tumor Analysis) terms and definitions. Histological outcome from surgery (n = 634) or long-term (≥ 3 years) follow-up (n = 124) served as the gold standard. The dataset was split into a training set (n = 508; 314 benign and 194 malignant), a validation set (n = 100; 60 benign and 40 malignant) and a test set (n = 150; 75 benign and 75 malignant). We used transfer learning on three pre-trained DNNs: VGG16, ResNet50 and MobileNet. Each model was trained, and the outputs calibrated, using temperature scaling. An ensemble of the three models was then used to estimate the probability of malignancy based on all images from a given case. The DNN ensemble classified the tumors as benign or malignant (Ovry-Dx1 model); or as benign, inconclusive or malignant (Ovry-Dx2 model). The diagnostic performance of the DNN models, in terms of sensitivity and specificity, was compared to that of SA for classifying ovarian tumors in the test set. RESULTS: At a sensitivity of 96.0%, Ovry-Dx1 had a specificity similar to that of SA (86.7% vs 88.0%; P = 1.0). Ovry-Dx2 had a sensitivity of 97.1% and a specificity of 93.7%, when designating 12.7% of the lesions as inconclusive. By complimenting Ovry-Dx2 with SA in inconclusive cases, the overall sensitivity (96.0%) and specificity (89.3%) were not significantly different from using SA in all cases (P = 1.0). CONCLUSION: Ultrasound image analysis using DNNs can predict ovarian malignancy with a diagnostic accuracy comparable to that of human expert examiners, indicating that these models may have a role in the triage of women with an ovarian tumor. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Studies with smaller sample sizes showed a level of performance reaching those of human experts.
“there were also studies with smaller sample sizes that showed a level of performance reaching those of human experts [188,191,192].”
Homogeneity of data due to a single examiner or single center study was a frequent limitation.
“Frequent described limitations of studies on AI applications in US imaging were homogeneity of data due to a single examiner or single center study [188,192]”
Clinicians using AI image analyzing algorithms must still take clinical aspects into account.
“clinicians using AI image analyzing algorithms must still take clinical aspects into account [188,189].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Interval Estimation for a Binomial Proportion
Brown, L. D., Cai, T., & Dasgupta, A. (2001) · Statistical Science · doi:10.1214/ss/1009213286 · Cited by 3,622 · see citing works
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167 · see citing works
A method for solving the convex programming problem with convergence rate O(1/k^2)
Nesterov, Y. E. (1983) · Medical Entomology and Zoology · Cited by 1,770 · see citing works
Effect of Screening on Ovarian Cancer Mortality
Buys, S. S. (2011) · JAMA · doi:10.1001/jama.2011.766 · Cited by 1,326 · see citing works
Timmerman, D., Valentin, L., Bourne, T., Collins, W. P., Verrelst, H., & Vergote, I. B. (2000) · Ultrasound in Obstetrics and Gynecology · doi:10.1046/j.1469-0705.2000.00287.x · Cited by 999 · see citing works
Epidemiology of epithelial ovarian cancer
Webb, P. M., & Jordan, S. J. (2016) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2016.08.006 · Cited by 998 · see citing works
Transfusion: Understanding Transfer Learning for Medical ImagingPreprint
Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 845 · see citing works
Simple ultrasound-based rules for the diagnosis of ovarian cancer
Timmerman, D., Testa, A. C., Bourne, T., Ameye, L., Jurkovic, D., Van Holsbeke, C., Paladini, D., Van Calster, B., Vergote, I., Van Huffel, S., et al. (2008) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.5365 · Cited by 613 · see citing works
Timmerman, D., Ameye, L., Fischerová, D., Epstein, E., Melis, G. B., Guerriero, S., Holsbeke, C. V., Savelli, L., Fruscio, R., Lissoni, A. A., et al. (2010) · BMJ · doi:10.1136/bmj.c6839 · Cited by 486 · see citing works
Self-citationLi, X., Zhang, S., Zhang, Q., Wei, X., Pan, Y., Zhao, J., Xin, X., Qin, C., Wang, X., Li, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30762-9 · Cited by 433 · see citing works
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1125-z · Cited by 381 · see citing works
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358 · see citing works
Timmerman, D., CALSTER, B. V., Testa, A. C., Savelli, L., Fischerová, D., Froyman, W., Wynants, L., Holsbeke, C. V., Epstein, E., Franchi, D., et al. (2016) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2016.01.007 · Cited by 337 · see citing works
Self-citationSurgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma
Engelen, M. J. A., Kos, H. E., Willemse, P. H. B., Aalders, J. G., de Vries, E. G. E., Schaapveld, M., Otter, R., & van der Zee, A. G. J. (2005) · Cancer · doi:10.1002/cncr.21616 · Cited by 305 · see citing works
Meys, E. M. J., Kaijser, J., Kruitwagen, R. F. P. M., Slangen, B. F. M., CALSTER, B. V., Aertgeerts, B., Verbakel, J. Y. J., Timmerman, D., & Gorp, T. V. (2016) · European Journal of Cancer · doi:10.1016/j.ejca.2016.01.007 · Cited by 273 · see citing works
Machine learning for medical ultrasound: status, methods, and future opportunities
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works
Froyman, W., Landolfo, C., Campo, B. D. C., Wynants, L., Sladkevicius, P., Testa, A. C., Holsbeke, C. V., Domali, E., Fruscio, R., Epstein, E., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30837-4 · Cited by 192 · see citing works
Self-citationDeep convolutional neural network for the diagnosis of thyroid nodules on ultrasound
Ko, S. Y., Lee, J. H., Yoon, J. H., Na, H., Hong, E., Han, K., Jung, I., Kim, E., Moon, H. J., Park, V. Y., et al. (2019) · Head & Neck · doi:10.1002/hed.25415 · Cited by 104 · see citing works
Valentin, L., Ameye, L., Savelli, L., Fruscio, R., Leone, F. P., Czekierdowski, A., Lissoni, A., Fischerová, D., Guerriero, S., Holsbeke, C. V., et al. (2011) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.9030 · Cited by 90 · see citing works
Sharma, A., Apostolidou, S., Burnell, M., Campbell, S. C., Habib, M., Gentry‐Maharaj, A., Amso, N. N., Seif, M. W., Fletcher, G., Singh, N., et al. (2012) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.12270 · Cited by 74 · see citing works
Martínez-Más, J., Bueno-Crespo, A., Khazendar, S., Remezal-Solano, M., Martínez-Cendán, J.-P., Jassim, S., Du, H., Al Assam, H., Bourne, T., & Timmerman, D. (2019) · PLOS ONE · doi:10.1371/journal.pone.0219388 · Cited by 70 · see citing works
Holsbeke, C. V., Daemen, A., Yazbek, J., Holland, T., Bourne, T., Mesens, T., Lannoo, L., Boes, A., Joos, A., van de Vijver, A., et al. (2009) · Gynecologic and Obstetric Investigation · doi:10.1159/000265012 · Cited by 69 · see citing works
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Roth, H., Myronenko, A., Xu, D., & Xu, Z. (2019) · arXiv · doi:10.48550/arxiv.1906.03347 · Cited by 36 · see citing works
Khazendar, S., Sayasneh, A., Al-Assam, H., Du, H., Kaijser, J., Ferrara, L., Timmerman, D., Jassim, S., & Bourne, T. (2015) · PubMed Central · Cited by 30 · see citing works
Khazendar, S., Al‐Assam, H., Du, H., et al. (2014) · Computer Science and Electronic Engineering Conference · doi:10.1109/ceec.2014.6958571 · Cited by 15 · see citing works
Grigore, M., Popovici, R. M., Gafițanu, D., Himiniuc, L. M., Murarasu, M., & Micu, R. (2020) · Medical Ultrasonography · doi:10.11152/mu-2538 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [26]Nesterov Y. A method of solving a convex programming problem with convergence rate O(1/k2). Sov Math Dokl 1983; 27: 372– 376.
- [27]Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015; 37: 448– 456. https://arxiv.org/abs/1502.03167. · arXiv
- [32]Ojala T, Pietikäinen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. Proceedings of 12th International Conference on Pattern Recognition (ICPR), Jerusalem, Israel, 1994; 1: 582– 585.
Cited by
Sorted by citations
Xu, H.-L., Gong, T., Liu, F.-H., Chen, H.-Y., Xiao, Q., Hou, Y., Huang, Y., Sun, H., Shi, Y., Gao, S., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101662 · Cited by 133 · see citing works
Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging
Komatsu, M., Sakai, A., Dozen, A., Shozu, K., Yasutomi, S., Machino, H., Asada, K., Kaneko, S., & Hamamoto, R. (2021) · Biomedicines · doi:10.3390/biomedicines9070720 · Cited by 119 · see citing works
A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)
Alamoudi, A., Khan, I. U., Aslam, N., Alqahtani, N., Alsaif, H. S., Dandan, O. S. A., Gadeeb, M. A., & Bahrani, R. A. (2023) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2023/9686697 · Cited by 111 · see citing works
Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment
Chen, H., Yang, B.-W., Qian, L., Meng, Y.-S., Bai, X.-H., Hong, X.-W., He, X., Jiang, M.-J., Yuan, F., Du, Q.-W., et al. (2022) · Radiology · doi:10.1148/radiol.211367 · Cited by 110 · see citing works
Shrestha, P., Poudyal, B., Yadollahi, S., Wright, D., Gregory, A., Warner, J., Korfiatis, P. D., Green, I. C., Rassier, S. L. C., Mariani, A., et al. (2022) · Gynecologic Oncology · doi:10.1016/j.ygyno.2022.07.024 · Cited by 100 · see citing works
International multicenter validation of AI-driven ultrasound detection of ovarian cancer
Christiansen, F., Konuk, E., Ganeshan, A. R., Welch, R., Huix, J. P., Czekierdowski, A., Leone, F. P. G., Haak, L. A., Fruscio, R., Gaurilčikas, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03329-4 · Cited by 72 · see citing works
Self-citationProgress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis
Li, Y., Li, Q., Fu, K., Zhou, X., & Zhang, K. (2025) · Bioengineering · doi:10.3390/bioengineering12030288 · Cited by 71 · see citing works
Jung, Y., Kim, T., Han, M.-R., Kim, S., Kim, G., Lee, S., & Choi, Y. J. (2022) · Scientific Reports · doi:10.1038/s41598-022-20653-2 · Cited by 67 · see citing works
Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis
Guerriero, S., Pascual, M. A., AJOSSA, S., Neri, M., Musa, E., Graupera, B., Rodríguez, I., & Alcázar, J. L. (2021) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2021.04.012 · Cited by 62 · see citing works
Jiang, Y., Wang, C., & Zhou, S. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.005 · Cited by 62 · see citing works
Westerlund, A. M., Hawe, J. S., Heinig, M., & Schunkert, H. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910291 · Cited by 61 · see citing works
Jan, Y., Tsai, P., Huang, W., Chou, L.-Y., Huang, S.-C., Wang, J., Lu, P.-H., Lin, D.-C., Yen, C.-S., Teng, J.-P., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01412-x · Cited by 61 · see citing works
Hsu, S.-T., Su, Y.-J., Hung, C.-H., Chen, M.-J., Lu, C.-H., & Kuo, C.-E. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02047-6 · Cited by 58 · see citing works
Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities
Sadeghi, M. H., Sina, S., Omidi, H., Farshchitabrizi, A. H., & Alavi, M. (2024) · Polish Journal of Radiology · doi:10.5114/pjr.2024.134817 · Cited by 54 · see citing works
Mitchell, S., Nikolopoulos, M., Elzarkaa, A. A., Al-Karawi, D., Al-Zaidi, S., Ghai, A., Gaughran, J., & Sayasneh, A. (2024) · Cancers · doi:10.3390/cancers16020422 · Cited by 52 · see citing works
Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
Moro, F., Ciancia, M., Zaçe, D., Vagni, M., Tran, H. E., Giudice, M. T., Zoccoli, S. G., Mascilini, F., Ciccarone, F., Boldrini, L., et al. (2024) · International Journal of Cancer · doi:10.1002/ijc.35092 · Cited by 51 · see citing works
Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)
Wang, Y., Lin, W., Zhuang, X., Wang, X., He, Y., Li, L., & Lyu, G. (2024) · Oncology Reports · doi:10.3892/or.2024.8705 · Cited by 45 · see citing works
Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“…homogeneity of data due to a single examiner or single center study [188,192], a single investigated ethnicity [189], absent external validation [192,193], poor image quality [186,190], and, most importantly, small sample sizes not sufficient enough to train the algorithm [184,185,187,188,192,193].”
BackgroundResultInfluentialApplication of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis
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., et al. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20031724 · Cited by 44 · see citing works
Ravishankar, T. N., Jadhav, H. M., Kumar, N. S., Ambala, S., & Pillai, N. M. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100797 · Cited by 41 · see citing works
Fischerová, D., Smet, C., Scovazzi, U., Sousa, D. N., Hundarova, K., & Haldorsen, I. S. (2024) · International Journal of Gynecological Cancer · doi:10.1136/ijgc-2023-004609 · Cited by 41 · see citing works
Singh, S., Maurya, M. K., Singh, N., & Kumar, R. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00491-0 · Cited by 39 · see citing works
Brandão, M., Mendes, F., Martins, M., Cardoso, P., Macedo, G., Mascarenhas, T., & Mascarenhas, M. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13041061 · Cited by 36 · see citing works
Wu, M., Cui, G., Lv, S., Chen, L., Tian, Z., Yang, M., & Bai, W. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1154200 · Cited by 35 · see citing works
Wang, Z., Luo, S., Chen, J., Jiao, Y., Cui, C., Shi, S., Yang, Y., Zhao, J., Jiang, Y., Zhang, Y., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109403 · Cited by 35 · see citing works
Du, Y., Guo, W., Xiao, Y., Chen, H., Yao, J., & Wu, J. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01251-2 · Cited by 33 · see citing works
Radhakrishnan, M., Sampathila, N., Muralikrishna, H., & Swathi, K. S. (2024) · IEEE Access · doi:10.1109/access.2024.3448219 · Cited by 33 · see citing works
Qi, L., Chen, D., Li, C., Li, J., Wang, J., Zhang, C., Li, X., Qiao, G., Wu, H., Zhang, X., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.753948 · Cited by 33 · see citing works
Ghi, T., Conversano, F., Ramirez Zegarra, R., Pisani, P., Dall'Asta, A., Lanzone, A., Lau, W., Vimercati, A., Iliescu, D. G., Mappa, I., et al. (2022) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.23739 · Cited by 32 · see citing works
Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01234-y · Cited by 32 · see citing works
Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting
Chen, L., Qiao, C., Wu, M., Cai, L., Yin, C., Yang, M., Sang, X., & Bai, W. (2023) · Bioengineering · doi:10.3390/bioengineering10020184 · Cited by 27 · see citing works
Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound
Barcroft, J. F., Linton‐Reid, K., Landolfo, C., Al‐Memar, M., Parker, N., Kyriacou, C., Munaretto, M., Fantauzzi, M., Cooper, N., Yazbek, J., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00527-8 · Cited by 25 · see citing works
Gandotra, S., Kumar, Y., Modi, N., Choi, J., Shafi, J., & Ijaz, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10872-6 · Cited by 24 · see citing works
New Trends in Ovarian Cancer Diagnosis Using Deep Learning: A Systematic Review
El-Khatib, M., Popescu, D., Teodor, O. M., & Ichim, L. (2024) · IEEE Access · doi:10.1109/access.2024.3434722 · Cited by 23 · see citing works
Koch, A. H., Jeelof, L. S., Muntinga, C. L. P., Gootzen, T. A., van de Kruis, N. M. A., Nederend, J., Boers, T. G. W., van der Sommen, F., & Piek, J. M. J. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01345-x · Cited by 23 · see citing works
Moro, F., Vagni, M., Tran, H. E., Bernardini, F., Mascilini, F., Ciccarone, F., Nero, C., Giannarelli, D., Boldrini, L., Fagotti, A., et al. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27680 · Cited by 23 · see citing works
Artificial intelligence in diagnostic ultrasonography
Dıcle, O. (2023) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2022.211260 · Cited by 22 · see citing works
Ovarian cancer data analysis using deep learning: A systematic review
Hira, M. T., Razzaque, M. A., & Sarker, M. H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109250 · Cited by 22 · see citing works
A Deep Learning Model System for Diagnosis and Management of Adnexal Masses
Li, J., Chen, Y., Zhang, M., Zhang, P., He, K., Yan, F., Li, J., Xu, H. L., Burkhoff, D., Luo, Y., et al. (2022) · Cancers · doi:10.3390/cancers14215291 · Cited by 21 · see citing works
Liu, L., Cai, W., Zhou, C., Tian, H., Wu, B., Zhang, J., Yue, G., & Hao, Y. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1362588 · Cited by 20 · see citing works
Updating methods for artificial intelligence–based clinical prediction models: a scoping review
Meijerink, L., Dunias, Z. S., Leeuwenberg, A. M., de Hond, A., Jenkins, D. A., Martin, G. P., Sperrin, M., Peek, N. B., Spijker, R., Hooft, L., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111636 · Cited by 19 · see citing works
Boyanapalli, A., & Shanthini, A. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7716 · Cited by 19 · see citing works
Dai, W., Wu, Y., Ling, Y.-T., Zhao, J., Zhang, S., Gu, Z.-W., Gong, L., Zhu, M.-N., Dong, S., Xu, S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102923 · Cited by 18 · see citing works
Liu, L., Cai, W., Tian, H., Wu, B., Zhang, J., Wang, T., Hao, Y., & Yue, G. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1377489 · Cited by 18 · see citing works
Xie, W., Lin, W., Li, P., Lai, H., Wang, Z., Liu, P., Huang, Y., Liu, Y., Tang, L., & Lyu, G. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-024-05872-6 · Cited by 18 · see citing works
Cetera, G. E., Tozzi, A. E., Chiappa, V., Castiglioni, I., Merli, C. E. M., & Vercellini, P. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13102950 · Cited by 17 · see citing works
Giourga, M., Petropoulos, I., Stavros, S., Potiris, A., Gerede, A., Sapantzoglou, I., Fanaki, M., Papamattheou, E., Karasmani, C., Karampitsakos, T., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13144123 · Cited by 17 · see citing works
Weichert, J., Welp, A., Scharf, J. L., Dracopoulos, C., Becker, W.-H., & Gembicki, M. (2021) · Geburtshilfe und Frauenheilkunde · doi:10.1055/a-1522-3029 · Cited by 17 · see citing works
Wan, F., He, W., Zhang, W., Zhang, Y., Zhang, H., & Yang, G. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01049-8 · Cited by 16 · see citing works
Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study
He, X., Bai, X.-H., Chen, H., & Feng, W. (2024) · Journal of Ovarian Research · doi:10.1186/s13048-024-01544-8 · Cited by 16 · see citing works
Fragomeni, S. M., Moro, F., Palluzzi, F., Mascilini, F., Rufini, V., Collarino, A., Inzani, F. S., Giacò, L., Scambia, G., Testa, A. C., et al. (2023) · Cancers · doi:10.3390/cancers15041121 · Cited by 16 · see citing works
Drukker, L. (2022) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.24869 · Cited by 16 · see citing works
Ferreira, I., Simões, J., Pereira, B., Correia, J. V. M., & Areia, A. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-65394-6 · Cited by 15 · see citing works
Ma, L., Huang, L., Chen, Y., Zhang, L., Nie, D., He, W., & Qi, X. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1133491 · Cited by 14 · see citing works
MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis
Burla, L., Sartoretti, E., Mannil, M., Seidel, S., Sartoretti, T., Krentel, H., Wilde, R. L. D., & Imesch, P. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13082344 · Cited by 13 · see citing works
Zeng, S., Jia, H., Zhang, H., Feng, X., Dong, M., Lin, L., Wang, X., & Yang, H. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00883-z · Cited by 12 · see citing works
Yaghouti, A. R. N., Shalbaf, A., Alizadehsani, R., Tan, R., Vijayananthan, A., Yeong, C. H., & Acharya, U. R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10268-x · Cited by 12 · see citing works
Fanizzi, A., Arezzo, F., Cormio, G., Comes, M. C., Cazzato, G., Boldrini, L., Bove, S., Bollino, M., Kardhashi, A., Silvestris, E., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.7425 · Cited by 11 · see citing works
Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy
Lugtu, E. J., Ramos, D. B., Agpalza, A. J., Cabral, E. A., Carandang, R. P., Dee, J. E., Martinez, A., Jose, J. E., Santillan, A., Bangaoil, R., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268329 · Cited by 11 · see citing works
Villamanca, J. J., Hermogino, L. J., Ong, K. D., Paguia, B., Abanilla, L., Lim, A., Angeles, L. M., Espiritu, B., Isais, M., Tomas, R. C., et al. (2022) · Applied Spectroscopy · doi:10.1177/00037028221116083 · Cited by 11 · see citing works
Early-fusion hybrid CNN-transformer models for multiclass ovarian tumor ultrasound classification
Garcia-Atutxa, I., Martínez-Más, J., Bueno-Crespo, A., & Villanueva‐Flores, F. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1679310 · Cited by 10 · see citing works
Garcia-Atutxa, I., Villanueva‐Flores, F., Barrio, E. D., Sanchez-Villamil, J. I., Martínez-Más, J., & Bueno-Crespo, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1649746 · Cited by 10 · see citing works
A Hybrid CNN-SVM Prediction Approach for Breast Cancer Ultrasound Imaging
Guizani, S., Guizani, N., & Gharsallaoui, S. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182874 · Cited by 10 · see citing works
Sood, M., Puthooran, E., & Jain, N. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10810-1 · Cited by 9 · see citing works
Nguyen, T.-B.-T., Vu-Xuan, A. T., & Ngo, V. M. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105604 · Cited by 9 · see citing works
de Andrade, A. C. F., da Silva, L. O., Souza, V. F., Rufino, L. M. D. A., da Silva, T. É., Santos, A., Gomes, D. D. A., & Rodrigues, J. P. P. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107674 · Cited by 8 · see citing works
Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network
Xi, M., Zheng, R., Wang, M., Shi, X., Chen, C., Qian, J., Gu, X., & Zhou, J. (2023) · Ginekologia Polska · doi:10.5603/gpl.94956 · Cited by 8 · see citing works
Sahu, G., Karnati, M., Rajput, A. S., Chaudhary, M., Maurya, R., & Dutta, M. K. (2023) · doi:10.1109/icsc60394.2023.10441101 · Cited by 8 · see citing works
Enhancing endometriosis care with telehealth: Opportunities and challenges
Perelmuter, S., & Shin, J. H. (2024) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x241287969 · Cited by 8 · see citing works
Deslandes, A., Avery, J., Chen, H., Leonardi, M., Condous, G. S., & Hull, M. L. (2023) · Australasian Journal of Ultrasound in Medicine · doi:10.1002/ajum.12368 · Cited by 7 · see citing works
Liu, L., Cai, W., Zheng, F., Tian, H., Li, Y., Wang, T., Chen, X., & Zhu, W. (2025) · Insights into Imaging · doi:10.1186/s13244-024-01874-7 · Cited by 7 · see citing works
Bhat, S., Birajdar, G. K., & Patil, M. D. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06606-0 · Cited by 7 · see citing works
Wang, Y., Zhang, J., He, Y., Wang, X., Wu, X., Zhang, W., Gong, M., Gao, D., Liu, S., Liu, P., et al. (2025) · Insights into Imaging · doi:10.1186/s13244-025-02112-4 · Cited by 7 · see citing works
Deeparani, M., & Kalamani, M. (2023) · Diagnostics · doi:10.3390/diagnostics13193131 · Cited by 7 · see citing works
RetractedФаридовна, А. Г., Danzanova, Т. Y., Mudunov, A. M., Bulanov, M. N., Шолохов, В. Н., Тимофеевна, С. Г., Gazhonova, V., Zubareva, E. A., Berdnikov, S. N., Лепэдату, П. И., et al. (2023) · Journal of oncology diagnostic radiology and radiotherapy · doi:10.37174/2587-7593-2023-6-4-42-52 · Cited by 7 · see citing works
Rousanuzzaman, R., Biswas, S. K., Saha, D., Thounaojam, D. M., Abhisheka, B., Das, S., & Purkayastha, B. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434294 · Cited by 7 · see citing works
Kamala, C., & Shivaram, J. M. (2024) · SN Computer Science · doi:10.1007/s42979-024-03272-8 · Cited by 6 · see citing works
Tang, C., Xu, Z., Duan, H., & Zhang, S. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1581157 · Cited by 6 · see citing works
Rashid, S., Karnati, M., Aggarwal, G., Dutta, M. K., Sikora, P., & Bürget, R. (2023) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt61075.2023.10333275 · Cited by 6 · see citing works
Ovarian Cancer Detection in CT Scan Images Using Transfer Learning
Awad, M. A., Al-Samrraie, L. A., Abdalla, A. M. A., AlZoubi, O., Migdady, A., & Yassein, M. B. (2023) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics60529.2023.10330434 · Cited by 6 · see citing works
Wang, X., Tao, Q., Chu, X., Gao, M., Zhang, Y., Chen, Y., Bai, G., Chen, S., & Wei, M. (2025) · Academic Radiology · doi:10.1016/j.acra.2024.12.067 · Cited by 5 · see citing works
A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer
Savitha, D., & Rajakumari, D. (2025) · International Research Journal of Multidisciplinary Technovation · doi:10.54392/irjmt2519 · Cited by 5 · see citing works
Srilatha, K., Jayasudha, F. V., Sumathi, M., & Chitra, P. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-1111-8_6 · Cited by 5 · see citing works
OCEAN - Ovarian Cancer subtypE clAssification and outlier detectioN using DenseNet121
Paayas, P., & Annamalai, R. (2023) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537795 · Cited by 5 · see citing works
Liu, T., Miao, K., Tan, G., Bu, H., Shao, X., Wang, S., & Dong, X. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.11.009 · Cited by 5 · see citing works
Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation
El-Khatib, M., Popescu, D., Teodor, O. M., & Ichim, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37386 · Cited by 4 · see citing works
OTC-NET: A Multimodal Method for Accurate Diagnosis of Ovarian Cancer in O-RADS Category 4 Masses
Liu, P., Ruan, Y., Fan, Y., Li, P., Liu, Z., Wu, S., Zheng, X., Wu, X., Liu, Y., & Liu, S. (2025) · Cancers · doi:10.3390/cancers17213466 · Cited by 4 · see citing works
Huang, D.-M., & Wang, S.-H. (2024) · Sensors · doi:10.3390/s24020363 · Cited by 4 · see citing works
Weighted Fusion Architecture for Improved Ovarian Tumor Segmentation from Ultrasound Images
Ta, N.-A., Ngo, V.-A., Pham, T.-L., Vu, D.-H., Le, T., Vu, H., Pham, Q. C., & Tran, T.-H. (2024) · International Conference on Communications and Electronics (ICCE) · doi:10.1109/icce62051.2024.10634737 · Cited by 4 · see citing works
Sajjad, R., Khan, M. F., Nawaz, A., Ali, M. T., & Adil, M. (2022) · Journal of Computing & Biomedical Informatics · doi:10.56979/302/2022/72 · Cited by 4 · see citing works
He, D., Long-hai, J., Geng, H., & Cao, L. (2025) · Human Pathology · doi:10.1016/j.humpath.2025.105762 · Cited by 3 · see citing works
Adnexal Lesion Discrimination Using Deep Learning Analysis of Dynamic Contrast-enhanced US Images
Wu, M., Yang, H., Chen, Y., Wu, S., Liang, T., Zhang, M., Qu, E., Sun, X., Zhang, R., Mu, L., et al. (2025) · Radiology Artificial Intelligence · doi:10.1148/ryai.240786 · Cited by 3 · see citing works
Lems, E., Koch, A. H., Delvaux, E. J. L. G., Leemans, J. C., Bongers, M. Y., Lok, C. A. R., Ramaekers, B. L. T., & Geomini, P. M. A. J. (2025) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.70135 · Cited by 3 · see citing works
A weakly-supervised follicle segmentation method in ultrasound images
Liu, G., Huang, W., Li, Y., Zhang, Q., Fu, J., Tang, H. L., Huang, J., Zhang, Z., Zhang, L., Wang, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95957-0 · Cited by 3 · see citing works
Identification of Benign Tumor Masses Using Deep Learning Techniques Based on Semantic Segmentation
El-Khatib, M., Teodor, O. M., Popescu, D., & Ichim, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43085-5_42 · Cited by 3 · see citing works
Artificial Intelligence and Obstetric Ultrasound
Matsuoka, R. (2021) · Donald School Journal of Ultrasound in Obstetrics & Gynecology · doi:10.5005/jp-journals-10009-1702 · Cited by 3 · see citing works
Characteristics of the reproductive potential of women with ovarian endometrioma
Dyndar, O. A., Dymarska, O. Z., & Oleshko, V. F. (2024) · Reproductive health of woman · doi:10.30841/2708-8731.4.2024.308995 · Cited by 2 · see citing works
Multi-level ensemble for enhanced prognosis of ovarian cancer from histopathological images
Singh, S., Kumar, R., Singh, N., & Maurya, M. K. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969298 · Cited by 2 · see citing works
R.Vijayakumar, Mithraa Ram, S., Varsha, R., & Vinothini, V. R. (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11170936 · Cited by 2 · see citing works
Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Shyamala Devi, M., Natarajan, Y., Sri Preethaa, K. R., Ganeshkumar, D., & Danushkumar, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-26314-4 · Cited by 2 · see citing works
- Authors
- Christiansen, F., Epstein, E. L., Smedberg, E., Åkerlund, M., Smith, K., & Epstein, E.
- Affiliations
- KTH Royal Institute of Technology (SE)
- Karolinska Institutet (SE)
- Harvard University (US)
- Science for Life Laboratory (SE)
- Corresponding author
- Epstein, E.
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 57
- Issue
- 1
- Pages
- 155-163
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- PMC
- PMC7839489
- OpenAlex
- W3096898300
- Cited by
- 142
- References
- 34
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 46 · 2023: 31 · 2022: 13 · 2021: 8
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Radiumhemmets Forskningsfonder (181115)
- Topics
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ovarian neoplasms, benign versus malignant differentiation, computer-aided diagnosis, deep neural network, transfer learning, ensemble learning, temperature scaling, subjective evaluation, power Doppler, B-mode ultrasound, ovarian cancer, diagnostic accuracy, histological outcomes, expert comparison, VGG16, ResNet50, MobileNet
- MeSH terms
- Deep Learning, Adnexal Diseases, Diagnosis, Differential, Female, Humans, Image Processing, Computer-Assisted, Ovarian Neoplasms, Retrospective Studies, Sensitivity and Specificity, Reproducibility of Results, Ultrasonography, Doppler
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
