{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132518"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132518","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Safe anatomy-driven tracking for navigation and robotics in medical and surgical procedures","abstract":"Medical imaging enables clinicians to visualize human anatomy and guide procedures with greater precision. However, existing navigation systems are prohibitively expensive, infrastructure-heavy, and difficult to operate, especially in resource-limited settings. This thesis explores these challenges by introducing two novel robotic platforms that leverage state-of-the-art computation, machine learning, and computer vision to deliver accurate image guidance for diagnostic and interventional medical procedures. The first system is a low-cost, articulated-arm neurosurgical navigation system for external ventricular drain (EVD) placement. Using a novel skull-mounting mechanism, the device provides forward-kinematic tracking of neurosurgical instruments, eliminating the need for traditional infrastructure-intensive tracking systems and enabling use in bedside emergency surgery settings. The second platform is an autonomous eye examination robot called EYESIGHT, designed to perform reliable, high-quality retinal photography on freestanding patients. By unifying robust human-tracking algorithms, closed-loop robotic control strategies, and user-friendly clinical workflows, these systems demonstrate how safe, anatomy-driven robotics can be developed to enable safe anatomy-driven system control and workflows in medical and surgical procedures. Phantom studies, ex-vivo validations, and preliminary user evaluations confirm that both platforms attain sub-millimeter accuracy while substantially mitigating workflow or resource requirements for both procedures described in this work. These findings open new frontiers for image-guided interventions in underserved clinics, emergency departments, and telemedicine environments. By bridging validation methodology advances with clinical demands, this thesis lays fundamental groundwork for broader, more equitable adoption of image-guided medical diagnostics and interventions globally. It proposes a framework for pre-clinical evaluation of novel systems through rapid prototyping, phantom testing, and iteration toward clinical evaluation. This testing framework encourages researchers in the intersection of medicine and technology to rapidly conceptualize and evaluate new systems.","abstract_html":"Medical imaging enables clinicians to visualize human anatomy and guide procedures with greater precision. However, existing navigation systems are prohibitively expensive, infrastructure-heavy, and difficult to operate, especially in resource-limited settings. This thesis explores these challenges by introducing two novel robotic platforms that leverage state-of-the-art computation, machine learning, and computer vision to deliver accurate image guidance for diagnostic and interventional medical procedures. The first system is a low-cost, articulated-arm neurosurgical navigation system for external ventricular drain (EVD) placement. Using a novel skull-mounting mechanism, the device provides forward-kinematic tracking of neurosurgical instruments, eliminating the need for traditional infrastructure-intensive tracking systems and enabling use in bedside emergency surgery settings. The second platform is an autonomous eye examination robot called EYESIGHT, designed to perform reliable, high-quality retinal photography on freestanding patients. By unifying robust human-tracking algorithms, closed-loop robotic control strategies, and user-friendly clinical workflows, these systems demonstrate how safe, anatomy-driven robotics can be developed to enable safe anatomy-driven system control and workflows in medical and surgical procedures. Phantom studies, ex-vivo validations, and preliminary user evaluations confirm that both platforms attain sub-millimeter accuracy while substantially mitigating workflow or resource requirements for both procedures described in this work. These findings open new frontiers for image-guided interventions in underserved clinics, emergency departments, and telemedicine environments. By bridging validation methodology advances with clinical demands, this thesis lays fundamental groundwork for broader, more equitable adoption of image-guided medical diagnostics and interventions globally. It proposes a framework for pre-clinical evaluation of novel systems through rapid prototyping, phantom testing, and iteration toward clinical evaluation. This testing framework encourages researchers in the intersection of medicine and technology to rapidly conceptualize and evaluate new systems.","abstract_has_math":false,"creators":["Smith, Alexander Daniel"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hauser, Kris","Arnold, Paul","Driggs-Campbell, Katie","Wang, Shenlong","Draelos, Mark"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Medical Robotics, Engineering and Medicine, Neurosurgery, Surgical Navigation, Image Guided Therapy, Ophthalmology, Optometry, Medical Imaging, Medical Devices, Medical Technology, Computer Science, Systems Engineering"],"languages":["en"],"rights":["© 2025 Alexander Daniel Smith"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132518","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris","Arnold, Paul","Driggs-Campbell, Katie","Wang, Shenlong","Draelos, Mark"]},{"key":"dc:creator","label":"Author","values":["Smith, Alexander Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-11-24"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Medical Robotics, Engineering and Medicine, Neurosurgery, Surgical Navigation, Image Guided Therapy, Ophthalmology, Optometry, Medical Imaging, Medical Devices, Medical Technology, Computer Science, Systems Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025 Alexander Daniel Smith"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132518"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Medical imaging enables clinicians to visualize human anatomy and guide procedures with greater precision. However, existing navigation systems are prohibitively expensive, infrastructure-heavy, and difficult to operate, especially in resource-limited settings. This thesis explores these challenges by introducing two novel robotic platforms that leverage state-of-the-art computation, machine learning, and computer vision to deliver accurate image guidance for diagnostic and interventional medical procedures. The first system is a low-cost, articulated-arm neurosurgical navigation system for external ventricular drain (EVD) placement. Using a novel skull-mounting mechanism, the device provides forward-kinematic tracking of neurosurgical instruments, eliminating the need for traditional infrastructure-intensive tracking systems and enabling use in bedside emergency surgery settings. The second platform is an autonomous eye examination robot called EYESIGHT, designed to perform reliable, high-quality retinal photography on freestanding patients. By unifying robust human-tracking algorithms, closed-loop robotic control strategies, and user-friendly clinical workflows, these systems demonstrate how safe, anatomy-driven robotics can be developed to enable safe anatomy-driven system control and workflows in medical and surgical procedures. Phantom studies, ex-vivo validations, and preliminary user evaluations confirm that both platforms attain sub-millimeter accuracy while substantially mitigating workflow or resource requirements for both procedures described in this work. These findings open new frontiers for image-guided interventions in underserved clinics, emergency departments, and telemedicine environments. By bridging validation methodology advances with clinical demands, this thesis lays fundamental groundwork for broader, more equitable adoption of image-guided medical diagnostics and interventions globally. It proposes a framework for pre-clinical evaluation of novel systems through rapid prototyping, phantom testing, and iteration toward clinical evaluation. This testing framework encourages researchers in the intersection of medicine and technology to rapidly conceptualize and evaluate new systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Alexander Smith, accepted the attached license on 2025-11-20 at 15:39.","The student, Alexander Smith, submitted this Dissertation for approval on 2025-11-21 at 17:41.","This Dissertation was approved for publication on 2025-11-24 at 09:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22916 on 2026-02-19 at 18:25:10"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Safe anatomy-driven tracking for navigation and robotics in medical and surgical procedures"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris","Arnold, Paul","Driggs-Campbell, Katie","Wang, Shenlong","Draelos, Mark"],"dc:creator":["Smith, Alexander Daniel"],"dc:date":["2025-12","2025-11-24"],"dc:description":["Medical imaging enables clinicians to visualize human anatomy and guide procedures with greater precision. However, existing navigation systems are prohibitively expensive, infrastructure-heavy, and difficult to operate, especially in resource-limited settings. This thesis explores these challenges by introducing two novel robotic platforms that leverage state-of-the-art computation, machine learning, and computer vision to deliver accurate image guidance for diagnostic and interventional medical procedures. The first system is a low-cost, articulated-arm neurosurgical navigation system for external ventricular drain (EVD) placement. Using a novel skull-mounting mechanism, the device provides forward-kinematic tracking of neurosurgical instruments, eliminating the need for traditional infrastructure-intensive tracking systems and enabling use in bedside emergency surgery settings. The second platform is an autonomous eye examination robot called EYESIGHT, designed to perform reliable, high-quality retinal photography on freestanding patients. By unifying robust human-tracking algorithms, closed-loop robotic control strategies, and user-friendly clinical workflows, these systems demonstrate how safe, anatomy-driven robotics can be developed to enable safe anatomy-driven system control and workflows in medical and surgical procedures. Phantom studies, ex-vivo validations, and preliminary user evaluations confirm that both platforms attain sub-millimeter accuracy while substantially mitigating workflow or resource requirements for both procedures described in this work. These findings open new frontiers for image-guided interventions in underserved clinics, emergency departments, and telemedicine environments. By bridging validation methodology advances with clinical demands, this thesis lays fundamental groundwork for broader, more equitable adoption of image-guided medical diagnostics and interventions globally. It proposes a framework for pre-clinical evaluation of novel systems through rapid prototyping, phantom testing, and iteration toward clinical evaluation. This testing framework encourages researchers in the intersection of medicine and technology to rapidly conceptualize and evaluate new systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Alexander Smith, accepted the attached license on 2025-11-20 at 15:39.","The student, Alexander Smith, submitted this Dissertation for approval on 2025-11-21 at 17:41.","This Dissertation was approved for publication on 2025-11-24 at 09:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22916 on 2026-02-19 at 18:25:10"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132518"],"dc:language":["en"],"dc:rights":["© 2025 Alexander Daniel Smith"],"dc:subject":["Medical Robotics, Engineering and Medicine, Neurosurgery, Surgical Navigation, Image Guided Therapy, Ophthalmology, Optometry, Medical Imaging, Medical Devices, Medical Technology, Computer Science, Systems Engineering"],"dc:title":["Safe anatomy-driven tracking for navigation and robotics in medical and surgical procedures"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}