title: Prediction of shape diameter undergoing coil embolization of saccular intracranial aneurysm treatment using a hybrid decision support system.
abstract: The purpose of the study was to design a hybrid decision support system (HDSS) that could simulate the embolized coil selection pattern of the radiologists in aneurysms treatment. As the longest available length of the coils should be used in most cases, therefore only the shape diameter (SD) selection was modeled and varied. Ninety-eight aneurysms successfully treated by a radiologist with coil embolization were divided into two groups (86 for training and 12 randomly selected for validating). Eight aneurysms treated by another radiologist were also used to cross validate the proposed HDSS. The HDSS was developed using the classification and the linear regression methods (LRM). The dome and the width of an aneurysm were used as the system inputs. The system outputs were the SDs of the first three coils indexed according to the insertion order. The HDSS that consisted of Bagging classification and LRM achieved the highest accuracy for all cases. The errors were within 1 mm for the SD selection of the first two coils. For the third coil, the SD selection within 1 mm bound had 80 % accuracy. The experimental results indicated the feasibility of using the HDSS as the guidance for selecting the SDs of the first two coils. The selection of the third coil required more training data for the rarely used SD. Moreover, the cross validation with another radiologist showed the feasibility of using the proposed HDSS as the guidance, however further validation with more data is recommended.
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand.
authors.forename: Wiwat
authors.initials: W
authors.lastname: Owasirikul
authors.forename: Jaturon
authors.initials: J
authors.lastname: Tantivatana
authors.forename: Duangrat
authors.initials: D
authors.lastname: Gansawat
authors.forename: Supatana
authors.initials: S
authors.lastname: Auethavekiat
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D004621:Embolization, Therapeutic / Q000295:instrumentation* / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D002532:Intracranial Aneurysm / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D011379:Prognosis; D017755:Prosthesis Fitting / Q000379:methods* / Q000706:statistics & numerical data*; D012307:Risk Factors; D013785:Thailand / Q000453:epidemiology; D016896:Treatment Outcome