{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84068"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84068","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Predicting Treatment Outcomes of Coiled Intracranial Aneurysms using Finite Element Modeling and Machine Learning","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Damiano, Robert; 0000-0003-2256-2154"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Meng, Hui","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:35Z","date_published":"2022-06-21T15:47:35Z","updated_at":"2026-07-27T19:05:30Z","subjects":["mechanical engineering","biomedical engineering","neurosciences"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84068","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meng, Hui","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Damiano, Robert; 0000-0003-2256-2154"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:35Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering","biomedical engineering","neurosciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84068"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Coil embolization is currently the predominate endovascular treatment modality for intracranial aneurysms (IAs). Coils, which are metallic spring-like devices that are implanted into the IA sac, obliterate the IA and exclude it from the cerebral circulation via thrombotic occlusion. Though endovascular coiling is a well-established IA treatment option and its use is wide-spread, it is not always effective at completely obliterating an IA. Incomplete occlusion of an IA after coiling is a major concern because it leaves the IA at risk of rupturing, the consequence of which is devastating bleeding in the patient’s brain. Unfortunately, in current clinical practice, there is no way to know whether a specific coil treatment strategy will lead to complete occlusion of an IA in time, and thus patients are subject to frequent and costly treatment follow-ups. However, if poor treatment outcomes could be predicted a priori, then clinicians could make better treatment decisions, which could potentially lead to improved outcomes and less need for treatment follow-ups. To that end, this work aimed to establish the feasibility of predicting outcomes of endovascular coiling of IAs using computer modeling and machine learning. To do so, we first developed and experimentally validated a state-of-the-art finite element method simulation technique that models endovascular coiling in patient-specific IAs. Next, we used this technique, in conjunction with computational fluid dynamics, to establish that incomplete occlusion of coiled IAs in the long-term is significantly associated with several morphologic, treatment-specific, and hemodynamic parameters. Finally, using machine learning, we established that predicting endovascular coiling outcomes with simulation-based parameters is possible, paving the way for the potential of simulation-based treatment planning and optimization in the future.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Predicting Treatment Outcomes of Coiled Intracranial Aneurysms using Finite Element Modeling and Machine Learning"]}]}],"canonical_facts":{"dc:contributor":["Meng, Hui","Mechanical and Aerospace Engineering"],"dc:creator":["Damiano, Robert; 0000-0003-2256-2154"],"dc:date":["2022-06-21T15:47:35Z","2020"],"dc:description":["Ph.D.","Coil embolization is currently the predominate endovascular treatment modality for intracranial aneurysms (IAs). Coils, which are metallic spring-like devices that are implanted into the IA sac, obliterate the IA and exclude it from the cerebral circulation via thrombotic occlusion. Though endovascular coiling is a well-established IA treatment option and its use is wide-spread, it is not always effective at completely obliterating an IA. Incomplete occlusion of an IA after coiling is a major concern because it leaves the IA at risk of rupturing, the consequence of which is devastating bleeding in the patient’s brain. Unfortunately, in current clinical practice, there is no way to know whether a specific coil treatment strategy will lead to complete occlusion of an IA in time, and thus patients are subject to frequent and costly treatment follow-ups. However, if poor treatment outcomes could be predicted a priori, then clinicians could make better treatment decisions, which could potentially lead to improved outcomes and less need for treatment follow-ups. To that end, this work aimed to establish the feasibility of predicting outcomes of endovascular coiling of IAs using computer modeling and machine learning. To do so, we first developed and experimentally validated a state-of-the-art finite element method simulation technique that models endovascular coiling in patient-specific IAs. Next, we used this technique, in conjunction with computational fluid dynamics, to establish that incomplete occlusion of coiled IAs in the long-term is significantly associated with several morphologic, treatment-specific, and hemodynamic parameters. Finally, using machine learning, we established that predicting endovascular coiling outcomes with simulation-based parameters is possible, paving the way for the potential of simulation-based treatment planning and optimization in the future.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84068"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering","biomedical engineering","neurosciences"],"dc:title":["Predicting Treatment Outcomes of Coiled Intracranial Aneurysms using Finite Element Modeling and Machine Learning"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}