AI-based analysis of the shunt treatment in pre- and post-surgery computed tomography brain scans of iNPH patients

Background This study examines whether quantifiable changes can be detected in ventricular volume in Idiopathic Normal Pressure Hydrocephalus (iNPH) patients that undergo ventriculo-peritoneal shunt procedures. There is no known metric that characterizes the change in ventricular volume for iNPH patients after shunt placement. Methods Two de-identified and independent datasets are studied: Results Our proposed metric achieves high accuracy (0.95), precision (0.96), and recall (0.96) in distinguishing between normal and iNPH subjects, surpassing the performance of the Evan’s Index. This metric allows us to track changes in ventricular volume before and after shunt surgery for 16 subjects. Notably, the 15 subjects with iNPH demonstrate a decrease in ventricular volume post-surgery and a concurrent clinical improvement in their iNPH symptomatology. Conclusion Our novel metric accurately quantifies changes in ventricular volume before and after shunt surgery for iNPH patients, serving as an effective radiographic marker for a functioning shunt in a patient with iNPH. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by NSF award: SSI # 1664172. We acknowledge the support of the Bisque team at UCSB and the Center for Artificial Intelligence in Diagnostic Medicine (CAIDM) team at UCI for their assistance with data management. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB agreement between UCI Medical Center and the University of California, Santa Barbara (UCSB) gave ethical approval for this work. This retrospective study is conducted with all images deidentified by the UCI Center for Artificial Intelligence in Diagnostic Medicine (CAIDM). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study will be made available after peer-review.