title: New method for identifying abnormal milling states of an otological drill.
abstract: Surgeons are continuing to strive toward achieving higher quality minimally invasive surgery. With the growth of modern technology, intelligent medical devices are being used to improve the safety of surgery. Milling beyond the bone tissue wall is a common abnormal milling state in ear surgery, as well as entanglement of the drill bit with the cotton swab, which will do harm to the patient's encephalic tissues. Various methods have been investigated by engineers and surgeons in an effort to avoid this type of abnormal milling state during surgery. This paper outlines a new method for identifying these two types of abnormal milling states. Five surgeons were invited to perform experiments on calvarial bones. The average recognition rate for otological drill milling through a bone tissue wall was 93%, with only 2% of normal millings being incorrectly identified as milling faults. The average recognition rate for entanglement of the drill bit with a cotton swab was 92%, with only 2% of normal millings being identified as milling faults. The method presented here can be adapted to the needs of the individual surgeon and reliably identify milling faults.
authors.affiliation: School of Automation and Electrical Engineering, University of Science and Technology, Beijing, People's Republic of China.
authors.forename: Yunqing
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Automation and Electrical Engineering, University of Science and Technology, Beijing, People's Republic of China.
authors.forename: Xisheng
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
authors.forename: Guodong
authors.initials: G
authors.lastname: Feng
authors.affiliation: Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Gao
authors.affiliation: Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
authors.forename: Peng
authors.initials: P
authors.lastname: Shen
keywords: intelligent system; multi-sensor information fusion; otological drill