{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:42:05Z","timestamp":1788183725402,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB020667"],"award-info":[{"award-number":["R01EB020667"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P41EB028741"],"award-info":[{"award-number":["P41EB028741"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate needle placement is essential for prostate biopsy. Recently, transperineal prostate biopsies are receiving renewed interest due to concern over infection from conventional transrectal biopsies. However, accurate needle placement is more challenging in the transperineal approach than in the transrectal approach due to the long insertion distance leading to a large targeting error and repeated insertion attempts. Improved procedure planning tools that can predict the deviation of the needle can potentially reduce the targeting error and number of insertion attempts. Prediction of deflection magnitude requires a model of biopsy needle deflection, which in turn requires information about tissue material properties. However, material properties of tissue in patients cannot be easily obtained. Accounting for this uncertainty in patient tissue properties requires a model capable of quantifying uncertainty in needle deflection as a function of a distribution of tissue properties. A Monte Carlo uncertainty quantification requires 1000s of samples, but it is not possible to obtain this many samples in a short enough time for intraoperative procedure planning using published needle deflection prediction\u00a0models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This work seeks to develop a model of needle deflection fast enough for use in intraoperative procedure planning, validate this model against experimental results, and integrate it into a Monte Carlo uncertainty quantification\u00a0model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This work used a mechanics\u2010based model of biopsy needle deflection to train a Fourier feature neural network (FFNN) model in order to make predictions with a low computational cost. Both models were validated against experimental data. The neural network model was used in a Monte Carlo uncertainty quantification model to quantify uncertainty in needle deflection arising from uncertain tissue mechanical\u00a0properties.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This work (1) implemented a mechanics\u2010based model and a FFNN model. Both models were validated against previously published experiments carried out with tissue phantoms. Both models showed close agreement with the experimental data. (2) We showed that our FFNN model was more accurate than a baseline ordinary least squares model, introducing only about 0.3\u2010mm tip deflection error compared to the mechanics\u2010based model. We also showed that our FFNN model makes unbiased predictions with respect to the amount of deflection. (3) We demonstrated a Monte Carlo uncertainty quantification model of needle deflection with a low computational cost of about 20 CPU s. We used our uncertainty quantification model to show how the depth, stiffness, and magnitude of uncertainty in a layer of tissue affect needle deflection. In addition, we showed a simple clinical example of the use of our model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This work demonstrates a Monte Carlo uncertainty quantification model of needle deflection with a low computational cost. This method shows promise for future applications in procedure planning for prostate biopsies as well as other transperineal procedures conducted with flexible needles such as cryoablation and\u00a0brachytherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70314","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T20:52:18Z","timestamp":1769892738000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning model for fast prediction and uncertainty quantification of needle deflection during prostate biopsy"],"prefix":"10.1002","volume":"53","author":[{"given":"Nathan","family":"Hoffman","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering University of Maryland College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidia","family":"Al\u2010Zogbi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Krieger","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Johns Hopkins University Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junichi","family":"Tokuda","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School Brigham and Women's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Moreira","sequence":"additional","affiliation":[{"name":"Department of Radiology, Harvard Medical School Brigham and Women's Hospital Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Fuge","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering University of Maryland College Park Maryland USA"},{"name":"Department of Mechanical and Process Engineering ETH Z\u00fcrich Z\u00fcrich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2016.08.004"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2013.05.059"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2100852"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1910038"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2012.12.057"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-016-2674-6"},{"key":"e_1_2_9_8_1","unstructured":"European Association of Eurology. 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