{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T19:25:58Z","timestamp":1785266758500,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry of Health through the \"Ricerca Corrente\" program granted to IRCCS Humanitas Research Hospital"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objective: High-frequency micro-ultrasound (micro-US) offers real-time, high-resolution imaging for prostate cancer. Although artificial intelligence (AI) has shown potential in enhancing micro-US interpretation, a comprehensive review of this emerging field is currently missing. This review synthesizes current evidence on AI applied to ExactVu 29 MHz micro-US for prostate cancer. Methods: PubMed\/MEDLINE, Embase, Scopus, Web of Science and the Cochrane Library were searched up to December 2025. Studies were included if they applied machine learning or deep learning directly to 29 MHz micro-US data and reported quantitative performance metrics. Results: Ten studies met the inclusion criteria: six on prostate cancer detection, three on prostate segmentation and one on micro-US\u2013histopathology registration. Detection models ranged from classical quantitative ultrasound machine learning to deep architectures using self-supervision, transformers, multiple-instance learning, ensemble calibration and 3D segmentation-based pipelines. Among core-level models for clinically significant cancer, area under the receiver operating characteristic curve (AUROC) values clustered around 0.76\u20130.81; one lesion-level framework reported an AUROC of 0.92, though at a non-comparable analytical unit. Segmentation studies achieved accurate prostate delineation (Dice similarity coefficient \u2248 0.94), and a single study demonstrated high-precision 3D registration to whole-mount histopathology (Dice similarity coefficient 0.97 and landmark error &lt; 3 mm). All studies evaluated AI on previously acquired data, without real-time clinical implementation. Conclusions: AI for micro-US shows promising and reproducible early results across detection, segmentation and registration, but evidence is still limited. In view of the potential of AI to optimize micro-US utilization and its related advantages, additional efforts are warranted to achieve clinical adoption.<\/jats:p>","DOI":"10.3390\/cancers18040665","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T09:22:46Z","timestamp":1771406566000},"page":"665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI in High-Frequency Micro-Ultrasound: Advancing Prostate Imaging from Segmentation to Cancer Detection"],"prefix":"10.3390","volume":"18","author":[{"given":"Ludovica","family":"Cella","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Paciotti","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pier Paolo","family":"Avolio","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6155-5964","authenticated-orcid":false,"given":"Vittorio","family":"Fasulo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6601-1244","authenticated-orcid":false,"given":"Andrea","family":"Piccolini","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Canneto","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Cavadini","sequence":"additional","affiliation":[{"name":"Medical School, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6295-0071","authenticated-orcid":false,"given":"Luca","family":"Di Stefano","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Saita","sequence":"additional","affiliation":[{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Casale","sequence":"additional","affiliation":[{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4411-3715","authenticated-orcid":false,"given":"Massimo","family":"Lazzeri","sequence":"additional","affiliation":[{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00f2 Maria","family":"Buffi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3939-4521","authenticated-orcid":false,"given":"Giovanni","family":"Lughezzani","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy"},{"name":"Department of Urology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/S1470-2045(19)30739-9","article-title":"Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study","volume":"21","author":"Bulten","year":"2020","journal-title":"Lancet Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","article-title":"Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer","volume":"2","author":"Nagpal","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100146","DOI":"10.1016\/j.cmpbup.2024.100146","article-title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","volume":"5","author":"Khalifa","year":"2024","journal-title":"Comput. Methods Programs Biomed. Update"},{"key":"ref_5","unstructured":"EAU Guidelines Office (2025). EAU Guidelines, EAU Guidelines Office."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1002\/pros.24862","article-title":"Optimizing Prostate Cancer Diagnostic Work-Up Through Micro-Ultrasound: Minimizing Unnecessary Procedures and Reducing Overdiagnoses","volume":"85","author":"Beatrici","year":"2025","journal-title":"Prostate"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.euros.2020.05.002","article-title":"Use of 29-MHz Micro-ultrasound for Local Staging of Prostate Cancer in Patients Scheduled for Radical Prostatectomy: A Feasibility Study","volume":"19","author":"Regis","year":"2020","journal-title":"Eur. Urol. Open Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"470.e19","DOI":"10.1016\/j.urolonc.2025.03.012","article-title":"Enhanced diagnostic accuracy of micro-ultrasound in prostate cancer detection: An updated series from a single-center prospective study","volume":"43","author":"Avolio","year":"2025","journal-title":"Urol. Oncol.: Semin. Orig. Investig."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1002\/pros.24532","article-title":"Diagnostic performance of microUltrasound at MRI-guided confirmatory biopsy in patients under active surveillance for low-risk prostate cancer","volume":"83","author":"Maffei","year":"2023","journal-title":"Prostate"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1007\/s00345-023-04480-2","article-title":"Diagnostic accuracy of multiparametric MRI- and microultrasound-targeted biopsy in biopsy-na\u00efve patients with a PI-RADS 5 lesion: A single-institutional study","volume":"41","author":"Avolio","year":"2023","journal-title":"World J. Urol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.euros.2024.08.013","article-title":"A Propensity Score\u2013matched Comparison of Micro-ultrasound\u2013guided Transrectal and Magnetic Resonance Imaging\/Transrectal Ultrasound Fusion-guided Transperineal Prostate Biopsies for Detection of Clinically Significant Prostate Cancer","volume":"69","author":"Piccolini","year":"2024","journal-title":"Eur. Urol. Open Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Beatrici, E., Frego, N., Chiarelli, G., Sordelli, F., Mancon, S., Saitta, C., De Carne, F., Garofano, G., Arena, P., and Avolio, P.P. (2024). A Comparative Evaluation of Multiparametric Magnetic Resonance Imaging and Micro-Ultrasound for the Detection of Clinically Significant Prostate Cancer in Patients with Prior Negative Biopsies. Diagnostics, 14.","DOI":"10.3390\/diagnostics14050525"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1001\/jama.2025.3579","article-title":"Microultrasonography-Guided vs MRI-Guided Biopsy for Prostate Cancer Diagnosis","volume":"333","author":"Kinnaird","year":"2025","journal-title":"JAMA"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pensa, J., Brisbane, W., Kinnaird, A., Kuppermann, D., Hughes, G., Ushko, D., Sisk, A., Marks, L., and Geoghegan, R. (2024). Deep Learning Classification of Prostate Cancer on Confidently Labeled Micro-Ultrasound Images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc.","DOI":"10.1109\/EMBC53108.2024.10782375"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1016\/j.ultrasmedbio.2018.02.014","article-title":"High-Frequency Quantitative Ultrasound for Imaging Prostate Cancer Using a Novel Micro-Ultrasound Scanner","volume":"44","author":"Rohrbach","year":"2018","journal-title":"Ultrasound Med. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s11548-023-02949-4","article-title":"TRUSformer: Improving prostate cancer detection from micro-ultrasound using attention and self-supervision","volume":"18","author":"Gilany","year":"2023","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11548-025-03335-y","article-title":"TRUSWorthy: Toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound","volume":"20","author":"Harmanani","year":"2025","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102326","DOI":"10.1016\/j.compmedimag.2024.102326","article-title":"MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images","volume":"112","author":"Jiang","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s11548-024-03119-w","article-title":"Toward confident prostate cancer detection using ultrasound: A multi-center study","volume":"19","author":"Wilson","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106657","DOI":"10.1016\/j.bspc.2024.106657","article-title":"Image registration of in vivo micro-ultrasound and ex vivo pseudo-whole mount histopathology images of the prostate: A proof-of-concept study","volume":"96","author":"Imran","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e3346","DOI":"10.7717\/peerj-cs.3346","article-title":"Hierarchically enhanced feature fusion and loss prevention for prostate segmentation on micro-ultrasound images","volume":"11","author":"Huang","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"AL-Qurri, A., Thaher, A., and Almekkawy, M.K. (2025). Enhanced Deep Neural Network for Prostate Segmentation in Micro-Ultrasound Images. Sensors, 25.","DOI":"10.3390\/s25226815"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1111\/bju.16892","article-title":"ProMUS-NET: Artificial intelligence detects more prostate cancer than urologists on micro-ultrasonography","volume":"136","author":"Zhou","year":"2025","journal-title":"BJU Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18910","DOI":"10.1038\/s41598-024-69804-7","article-title":"Evaluation of prostate cancer detection using micro-ultrasound versus MRI through co-registration to whole-mount pathology","volume":"14","author":"Pensa","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e339","DOI":"10.1016\/j.clgc.2022.01.013","article-title":"Comparison Between Micro-Ultrasound and Multiparametric MRI Regarding the Correct Identification of Prostate Cancer Lesions","volume":"20","author":"Lorusso","year":"2022","journal-title":"Clin. Genitourin. Cancer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"E11","DOI":"10.5489\/cuaj.6712","article-title":"Comparison of micro-ultrasound and multiparametric magnetic resonance imaging for prostate cancer: A multicenter, prospective analysis","volume":"15","author":"Klotz","year":"2020","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1097\/JU.0000000000001639","article-title":"Micro-Ultrasound\u2013Guided vs Multiparametric Magnetic Resonance Imaging-Targeted Biopsy in the Detection of Prostate Cancer: A Systematic Review and Meta-Analysis","volume":"205","author":"Sountoulides","year":"2021","journal-title":"J. Urol."},{"key":"ref_28","unstructured":"Lucida Medical Ltd. (AI Software for Prostate MRI Detection and Triage, 2023). AI Software for Prostate MRI Detection and Triage, Pi\u2122, CE-Marked edition."},{"key":"ref_29","unstructured":"Quantib B.V. (AI Software for Prostate MRI Interpretation, 2023). AI Software for Prostate MRI Interpretation, Quantib Prostate, FDA 510(k) K200876, CE-certified edition."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2515672","DOI":"10.1001\/jamanetworkopen.2025.15672","article-title":"AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images","volume":"8","author":"Twilt","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/4\/665\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T13:00:35Z","timestamp":1771419635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/4\/665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["cancers18040665"],"URL":"https:\/\/doi.org\/10.3390\/cancers18040665","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]}}}