{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:29:21Z","timestamp":1793158161062,"version":"4.5.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"AICTE- Research Promotion Scheme","award":["8-98\/FDC\/RPS\/POLICY-1\/2021-22 (AQIS ID: 1-9331521061)"],"award-info":[{"award-number":["8-98\/FDC\/RPS\/POLICY-1\/2021-22 (AQIS ID: 1-9331521061)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-01744-2","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T22:20:48Z","timestamp":1747261248000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images"],"prefix":"10.1038","volume":"15","author":[{"given":"S.","family":"Reka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T. Suriya","family":"Praba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukesh","family":"Prasanna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanipenta Naga Nithin","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rengarajan","family":"Amirtharajan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"9","key":"1744_CR1","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1093\/humrep\/dey256","volume":"33","author":"HJ Teede","year":"2018","unstructured":"Teede, H. J. et al. Recommendations from the international evidence-based guideline for the assessment and management of poly-cystic ovary syndrome. Hum. Reprod. 33(9), 1602\u20131618. https:\/\/doi.org\/10.1093\/humrep\/dey256 (2018).","journal-title":"Hum. Reprod."},{"key":"1744_CR2","doi-asserted-by":"publisher","unstructured":"Azziz, R. et al. The androgen excess and PCOS Society criteria for the poly-cystic ovary syndrome: The complete task force report. Fertil. Steril.   91(2). https:\/\/doi.org\/10.1016\/j.fertnstert.2008.06.035 (2009).","DOI":"10.1016\/j.fertnstert.2008.06.035"},{"issue":"1","key":"1744_CR3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.fertnstert.2003.10.004","volume":"81","author":"BCJM Fauser","year":"2004","unstructured":"Fauser, B. C. J. M. Revised 2003 consensus on diagnostic criteria and long-term health risks related to poly-cystic ovary syndrome. Fertil. Steril. 81(1), 19\u201325. https:\/\/doi.org\/10.1016\/j.fertnstert.2003.10.004 (2004).","journal-title":"Fertil. Steril."},{"issue":"1","key":"1744_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10916-023-01983-8","volume":"47","author":"PA GhoshRoy, Debasmita","year":"2023","unstructured":"GhoshRoy, Debasmita, P. A., Alvi & Santosh, K. C. AI tools for assessing human fertility using risk factors: A state-of-the-art review. J. Med. Syst. 47(1), 91 (2023).","journal-title":"J. Med. Syst."},{"key":"1744_CR5","doi-asserted-by":"publisher","unstructured":"Rachana, B. et al. Detection of poly-cystic ovarian syndrome using follicle recognition technique. Glob. Trans. Proc. 2(2), 304\u2013308. https:\/\/doi.org\/10.1016\/j.gltp.2021.08.010 (2021).","DOI":"10.1016\/j.gltp.2021.08.010"},{"key":"1744_CR6","doi-asserted-by":"publisher","first-page":"106874","DOI":"10.1016\/j.cmpb.2022.106874","volume":"221","author":"J Egger","year":"2022","unstructured":"Egger, J. et al. Medical deep learning\u2014a systematic meta-review. Comput. Methods Programs Biomed. 221, 106874 (2022).","journal-title":"Comput. Methods Programs Biomed."},{"issue":"9","key":"1744_CR7","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.jacr.2019.06.004","volume":"16","author":"Z Akkus","year":"2019","unstructured":"Akkus, Z. et al. A survey of deep-learning applications in ultrasound: Artificial intelligence\u2013powered ultrasound for improving clinical workflow. J. Am. Coll. Radiol. 16(9), 1318\u20131328 (2019).","journal-title":"J. Am. Coll. Radiol."},{"issue":"4","key":"1744_CR8","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1007\/s41870-024-01773-6","volume":"16","author":"M Jha","year":"2024","unstructured":"Jha, M., Gupta, R. & Saxena, R. Noise cancellation of poly-cystic ovarian syndrome ultrasound images using robust two-dimensional fractional fourier transform filter and VGG-16 model. Int. J. Inform. Technol. 16 (4), 2497\u20132504. https:\/\/doi.org\/10.1007\/s41870-024-01773-6 (2024).","journal-title":"Int. J. Inform. Technol."},{"issue":"January","key":"1744_CR9","doi-asserted-by":"publisher","first-page":"25740","DOI":"10.1109\/ACCESS.2024.3363172","volume":"12","author":"J Liu","year":"2024","unstructured":"Liu, J. & Chandrasiri, N. P. CA-ESRGAN: Super-resolution image synthesis using channel attention-based ESRGAN. IEEE Access 12(January), 25740\u201325748. https:\/\/doi.org\/10.1109\/ACCESS.2024.3363172 (2024).","journal-title":"IEEE Access"},{"issue":"PC","key":"1744_CR10","doi-asserted-by":"publisher","first-page":"123795","DOI":"10.1016\/j.eswa.2024.123795","volume":"249","author":"C Wang","year":"2024","unstructured":"Wang, C., Chen, H., Zhou, X., Wang, M. & Zhang, Q. SAM-IE: SAM-based image enhancement for facilitating medical image diagnosis with segmentation foundation model. Expert Syst. Appl. 249(PC), 123795. https:\/\/doi.org\/10.1016\/j.eswa.2024.123795 (2024).","journal-title":"Expert Syst. Appl."},{"key":"1744_CR11","unstructured":"Wang, C. et al. SAMMed: A medical image annotation framework based on large vision model. (2023). http:\/\/arxiv.org\/abs\/2307.05617."},{"key":"1744_CR12","doi-asserted-by":"publisher","unstructured":"Wang, C. J., He, C. S., Yan, R. X. & Liu, Y. C. Application of MP-YOLO for segmentation and visualization of ovarian ultrasound imaging.  in 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2023 130\u2013132. (2023). https:\/\/doi.org\/10.1109\/ECBIOS57802.2023.10218459.","DOI":"10.1109\/ECBIOS57802.2023.10218459"},{"issue":"6","key":"1744_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/healthcare11060863","volume":"11","author":"G Alwakid","year":"2023","unstructured":"Alwakid, G., Gouda, W. & Humayun, M. Deep learning-based prediction of diabetic retinopathy using CLAHE and ESRGAN for enhancement. Healthc. (Switzerland)  11(6), 1\u201317. https:\/\/doi.org\/10.3390\/healthcare11060863 (2023).","journal-title":"Healthc. (Switzerland)"},{"key":"1744_CR14","doi-asserted-by":"publisher","unstructured":"Nazarudin, A. A. et al. Performance analysis of a novel hybrid segmentation method for poly-cystic ovarian syndrome monitoring. Diagnostics 13(4). https:\/\/doi.org\/10.3390\/diagnostics13040750 (2023).","DOI":"10.3390\/diagnostics13040750"},{"key":"1744_CR15","doi-asserted-by":"crossref","unstructured":"Marinov, Z., J\u00e4ger, P. F., Egger, J., Kleesiek, J. & Stiefelhagen, R. Deep interactive segmentation of medical images: A systematic review and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. (2024).","DOI":"10.1109\/TPAMI.2024.3452629"},{"key":"1744_CR16","doi-asserted-by":"publisher","first-page":"104791","DOI":"10.1016\/j.bspc.2023.104791","volume":"84","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X. & Zhang, Q. Transformers in medical image segmentation: A review. Biomed. Signal Process. Control  84, 104791 (2023).","journal-title":"Biomed. Signal Process. Control"},{"key":"1744_CR17","unstructured":"Kirillov, A. et al. Segment Anything 4015\u20134026 (n.d)."},{"key":"1744_CR18","doi-asserted-by":"publisher","unstructured":"Qian, J. et al. HASA: Hybrid architecture search with aggregation strategy for echinococcosis classification and ovary segmentation in ultrasound images. Expert Syst. Appl. 202(October 2021), 117242.  https:\/\/doi.org\/10.1016\/j.eswa.2022.117242 (2022).","DOI":"10.1016\/j.eswa.2022.117242"},{"issue":"May","key":"1744_CR19","doi-asserted-by":"publisher","first-page":"100039","DOI":"10.1016\/j.bea.2022.100039","volume":"3","author":"A Sawant","year":"2022","unstructured":"Sawant, A. & Kulkarni, S. Ultrasound image enhancement using super resolution. Biomedical Eng. Adv. 3(May), 100039. https:\/\/doi.org\/10.1016\/j.bea.2022.100039 (2022).","journal-title":"Biomedical Eng. Adv."},{"issue":"23\u201324","key":"1744_CR20","doi-asserted-by":"publisher","first-page":"17169","DOI":"10.1007\/s11042-019-07762-3","volume":"79","author":"C Gopalakrishnan","year":"2020","unstructured":"Gopalakrishnan, C. & Iyapparaja, M. Active contour with modified Otsu method for automatic detection of poly-cystic ovary syndrome from ultrasound image of ovary. Multimedia Tools Appl. 79(23\u201324), 17169\u201317192. https:\/\/doi.org\/10.1007\/s11042-019-07762-3 (2020).","journal-title":"Multimedia Tools Appl."},{"key":"1744_CR21","doi-asserted-by":"publisher","unstructured":"Bedi, P., Goyal, S. B., Rajawat, A. S. & Kumar, M. An integrated adaptive bilateral filter-based framework and attention residual U-net for detecting poly-cystic ovary syndrome. Dec.. Anal. J. 10(June 2023) . https:\/\/doi.org\/10.1016\/j.dajour.2023.100366 (2024).","DOI":"10.1016\/j.dajour.2023.100366"},{"key":"1744_CR22","doi-asserted-by":"publisher","unstructured":"Reka, S., Mohan, K. & Praba, T. S. Prompt diagnosis of poly-cystic ovary syndrome using ultrasonography\u2014a machine learning approach. AIP Conf. Proc. 3031(1). https:\/\/doi.org\/10.1063\/5.0194703 (2024).","DOI":"10.1063\/5.0194703"},{"key":"1744_CR23","doi-asserted-by":"publisher","unstructured":"Alamoudi, A. et al. A deep learning fusion approach to diagnosis the Poly-cystic ovary syndrome (PCOS). Appl. Comput. Intell. Soft Comput. 2023 https:\/\/doi.org\/10.1155\/2023\/9686697 (2023).","DOI":"10.1155\/2023\/9686697"},{"key":"1744_CR24","doi-asserted-by":"publisher","unstructured":"Chitra, P. et al. Classification of ultrasound PCOS image using deep learning based hybrid models. in Proc. 2023 2nd Int. Conf. Electron. Renew. Syst. ICEARS 2023  1389-1394  (2023).https:\/\/doi.org\/10.1109\/ICEARS56392.2023.10085400.","DOI":"10.1109\/ICEARS56392.2023.10085400"},{"key":"1744_CR25","doi-asserted-by":"publisher","unstructured":"Salman Hosain, A. K. M., Mehedi, M. H. K. & Kabir, I. E.  Technologies, I. C. E. E. T. PCONet: A convolutional neural network architecture to detect poly-cystic ovary syndrome (PCOS) from ovarian ultrasound images.  in 8th International Conference on Engineering and Emerging 2022, October, 1\u20136. (2022). https:\/\/doi.org\/10.1109\/ICEET56468.2022.10007353.","DOI":"10.1109\/ICEET56468.2022.10007353"},{"key":"1744_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-021-01203-x","author":"C Gopalakrishnan","year":"2021","unstructured":"Gopalakrishnan, C. & Iyapparaja, M. Multilevel thresholding based follicle detection and classification of poly-cystic ovary syndrome from the ultrasound images using machine learning. Int. J. Syst. Assur. Eng. Manage. https:\/\/doi.org\/10.1007\/s13198-021-01203-x (2021).","journal-title":"Int. J. Syst. Assur. Eng. Manage."},{"key":"1744_CR27","doi-asserted-by":"crossref","unstructured":"Wang, X. et al. and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. in Proceedings of the European conference on computer vision (ECCV) workshops  (2018).","DOI":"10.1007\/978-3-030-11021-5_5"},{"issue":"3","key":"1744_CR28","doi-asserted-by":"publisher","first-page":"230","DOI":"10.5582\/bst.2023.01119","volume":"17","author":"G Ning","year":"2023","unstructured":"Ning, G., Liang, H., Jiang, Z., Zhang, H. & Liao, H. The potential of segment anything (SAM) for universal intelligent ultrasound image guidance. Biosci. Trends 17(3), 230\u2013233. https:\/\/doi.org\/10.5582\/bst.2023.01119 (2023).","journal-title":"Biosci. Trends"},{"key":"1744_CR29","doi-asserted-by":"publisher","first-page":"25012","DOI":"10.1038\/s41598-024-75964-3","volume":"14","author":"P Moral","year":"2024","unstructured":"Moral, P. et al. CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images. Sci. Rep. 14, 25012. https:\/\/doi.org\/10.1038\/s41598-024-75964-3 (2024).","journal-title":"Sci. Rep."},{"key":"1744_CR30","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1186\/s12911-024-02688-9","volume":"24","author":"K Shanmugavadivel","year":"2024","unstructured":"Shanmugavadivel, K. & R, M. S. M. D. T. Optimised polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data. BMC Med. Inf. Decis. Mak. 24, 281. https:\/\/doi.org\/10.1186\/s12911-024-02688-9 (2024).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"1744_CR31","doi-asserted-by":"publisher","unstructured":"Reka, S., Suriya, P. T. & Mohan, K. Expeditious prognosis of PCOS with ultrasonography images\u2014a convolutional neural network approach. Commun. Comput. Inf. Sci. 1929, 367\u2013376.  https:\/\/doi.org\/10.1007\/978-3-031-48774-3_26 (2024).","DOI":"10.1007\/978-3-031-48774-3_26"},{"key":"1744_CR32","unstructured":"Dataset. https:\/\/www.kaggle.com\/datasets\/anaghachoudhari\/pcos-detection-using-ultrasound-images."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01744-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01744-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01744-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T22:20:53Z","timestamp":1747261253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01744-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1744"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-01744-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]},"assertion":[{"value":"26 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article contains no studies with human participants performed by authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethical approval"}}],"article-number":"16832"}}