{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T11:50:24Z","timestamp":1791719424819,"version":"4.3.4"},"reference-count":29,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003845","name":"Indian Institute of Technology Madras","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003845","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate path planning for minimally invasive neurosurgery is necessary to navigate complex anatomy that contains critical structures, such as blood vessels, ventricles and white\u2010matter tracts. Planning with nonholonomic kinematics is difficult, and typical planners fail to generate kinematically feasible paths.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>AutoPlan is a deep reinforcement learning (DRL) framework that generates a needle trajectory on a magnetic resonance imaging (MRI) patient dataset. AutoPlan implements kinematic control strategies, variable curvature with fixed insertion (VCFI) and variable insertion with fixed curvature (VIFC), with random and selective start\u2010point initialisation. Path planning was performed on 55 volumes using PPO, SAC, DDPG and RRT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>PPO (VIFC, selective start): 90.9% success, path length 45.96  14.76\u00a0mm, computation time 0.02  0.02\u00a0s. RRT: 65.5% success, path length 102.07  126.15\u00a0mm, computation time 9.72  20.29\u00a0s.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AutoPlan provides consistent planning while avoiding critical structures, resulting in fast, kinematically safe trajectories.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/rcs.70239","type":"journal-article","created":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T11:40:16Z","timestamp":1791718816000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AutoPlan: Autonomous Surgical Planning Framework for Neurosurgery Interventions With Steerable Needles"],"prefix":"10.1002","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7975-6242","authenticated-orcid":false,"given":"Pranali","family":"Ingole","sequence":"first","affiliation":[{"name":"Department of Engineering Design Indian Institute of Technology Madras  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8113-6078","authenticated-orcid":false,"given":"Niravkumar","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Engineering Design Indian Institute of Technology Madras  Chennai Tamil Nadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,11]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.2147\/rsrr.s224446"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10143\u2010012\u20100436\u20108"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128423","article-title":"Recent Progress, Challenges and Future Prospects of Applied Deep Reinforcement Learning: A Practical Perspective in Path Planning","author":"Zhang Y.","year":"2024","journal-title":"Neurocomputing"},{"key":"e_1_2_12_5_1","doi-asserted-by":"crossref","DOI":"10.1002\/rcs.2576","article-title":"A New Surgical Path Planning Framework for Neurosurgery","author":"Pehlivano\u011flu M. 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