Automatic identification of otological drilling faults: an intelligent recognition algorithm

Cao, Tianyang; Li, Xisheng; Gao, Zhiqiang; Feng, Guorui; Shen, Peng

doi:10.1002/rcs.312

Cited by 15 publications

(31 citation statements)

References 17 publications

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“…A sleeve was added in front of the drill handle [8]. Through a sliding bearing installed in the front of the sleeve, the normal cutting force F of the drill bit can be transferred to the sleeve (Figure 1).…”

Section: Sensor Installation and Experiments Designmentioning

confidence: 99%

“…Because a rapid increase in current is one of the important indicators of cotton swab entanglement [7,8], this paper need only analyze the process by which the resistance moment increases. When the drill bit is milling bone, T zn will continue to increase for a short time.…”

Section: Feature Analysis and Extractionmentioning

confidence: 99%

“…For example, with the help of a clutch-based system, drills used in craniotomy can stop drilling when the skull is penetrated. In recent years, multi-sensor information fusion technology has been widely used in the medical field [2][3][4][5][6][7][8][9]. For instance, British researchers have designed an intelligent drill which, responding to data from torque and force sensors, can stop drilling before the bone is penetrated [5].…”

Section: Introductionmentioning

confidence: 99%

“…The recognition method presented in this paper has also been applied to the design of an intelligent drill for use in milling bone, a process that is more complicated than drilling holes [8,9]. The drill is operated by the surgeon's hand, and moves back and forth on the bone surface.…”

Section: Introductionmentioning

confidence: 99%

“…The drill is operated by the surgeon's hand, and moves back and forth on the bone surface. The drill bit rotates rapidly in a counterclockwise direction and can mill the bone layer by layer [8]. However, during the milling process, it is easy for the drill bit to become entangled with a cotton swab.…”

Section: Introductionmentioning

confidence: 99%

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A method for identifying otological drill entanglement with a cotton swab

Cao

Gao

et al. 2011

Computer Aided Surgery

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Background: The entanglement of the otological drill with cotton swabs is a common milling fault in ear surgery. To improve operational safety, this paper presents a method for identifying this type of milling fault. Methods: Force and current sensors were installed on a modified otological drill. In accordance with the DC motor model and cutting force model, two features of the milling process were extracted, namely the characteristic curve and the dynamic relationship between the sensor signals. These are complementary features. An adaptive filter was designed to fuse them together and output a curve that was sensitive to milling faults and was stable during normal milling. Based on the filtering data, a rule base is presented for identifying cotton swab entanglement. Results: Five surgeons were invited to perform an experiment on calvarian bones. The average recognition rate for milling faults was 90%, whereas only 2% of normal millings were identified as milling faults. Conclusions: The presented method could adapt to the technique of different surgeons and identify milling faults exactly.

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Section: Sensor Installation and Experiments Designmentioning

confidence: 99%

Section: Feature Analysis and Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A method for identifying otological drill entanglement with a cotton swab

Cao

Gao

et al. 2011

Computer Aided Surgery

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Design and performance study of an orthopaedic surgery robotized module for automatic bone drilling

Boiadjiev

Kastelov

Boiadjiev

et al. 2013

Int J Med Robotics Comput Assist Surg

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Automatic bone drilling could entirely solve the problems that usually arise during manual drilling. An experimental setup was designed to identify bone drilling parameters such as the resistance force arising from variable bone density, appropriate mechanical drilling torque, linear speed of the drill, and electromechanical characteristics of the motors, drives, and corresponding controllers. Automatic drilling guarantees greater safety for the patient. Moreover, the robot presented is user-friendly because it is simple to set robot tasks, and process data are collected in real time.

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A model‐based bone milling state identification method via force sensing for a robotic surgical system

Al-Abdullah

Lim

Najdovski

et al. 2019

Robotics Computer Surgery

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BackgroundThis paper presents a model‐based bone milling state identification method that provides intraoperative bone quality information during robotic bone milling. The method helps surgeons identify bone layer transitions during bone milling.MethodsOn the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.ResultsThe verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.ConclusionsThe significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.

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Automatic identification of otological drilling faults: an intelligent recognition algorithm

Abstract: This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions.

Cited by 15 publications

References 17 publications

A method for identifying otological drill entanglement with a cotton swab

A method for identifying otological drill entanglement with a cotton swab

Design and performance study of an orthopaedic surgery robotized module for automatic bone drilling

A model‐based bone milling state identification method via force sensing for a robotic surgical system

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