The Preliminary Research Of An Intelligent Otologic Drill

BackgroundIn otologic surgery, the high-speed rotating drill bit is easy to touch important healthy structures and cause collateral damage, so minimizing or avoiding damage caused by loss of control during drilling is an important issue. Thus far, some navigational or robotic concepts for guiding the drill have been pursued in experimental temporal bone surgery, but certain key problems have not been resolved, and these studies have not yet led to clinical application.Practical experience of otologic surgery indicates that damage to important healthy structures is most likely to occur during drilling faults, such as drill bit slippage, drilling through the bone tissue wall and cotton swab entanglement. Using multi-sensor information fusion and weak signal detection techniques, we want to explore the change rule of forces acting on the drill bit during normal drilling (including normal removal of the drill bit from the working surface), drill bit slippage, drilling through the bone tissue wall and cotton swab entanglement, and use the rule to identify the drilling faults in real time and make an intelligent otologic drill finally.The rapid development and wide application of multi-sensor information fusion and weak signal detection techniques ensure the success of this research.Materials and MethodsBased on the analysis of forces acting on the drill bit when drilling, the first otologic drill was modified and equipped with force sensor, current sensor, voltage sensor and speed sensor. Under consistent conditions (the same surgeon, maximum voltage and a 3mm diameter cutting burr), the modified drill was used to simulate four different drilling scenarios in otologic surgery, including normal drilling (including normal removal of the drill bit from the working surface), drill bit slippage, drilling through the bone tissue wall and cotton swab entanglement, each scenario was repeated about 1000 times on fresh porcine scapulas. During the trials all sensor signals were recorded and analyzed the rule, then the significant sudden changes in the signals were extracted as characteristic signals. A multi-sensor information fusion system was designed to identify the drilling faults and to add up the identification rate, and a stop program was designed to make the drill stop drilling when the drilling faults were identified.Then the second otologic drill was modified and equipped with force sensor, current sensor and voltage sensor. Under different conditions(the maximum voltage,10 different otologic doctors, cutting burr of 2.5mm,4mm,5.9mm diameter and a 4.2mm diameter diamond bit), each doctor used each kind of drill bit to simulate each scenario for 100 times on the cadaveric temporal bone, all sensor signals were recorded and analyzed. The information fusion system was used to identify the drilling faults and to add up the identification rate.ResultsUnder consistent conditions, the signal of each sensor changed consistently during each drilling scenario, with high repeatability and regularity of signal variation. It is possible to extract a characteristic signal for each kind of drilling fault. Using our multi-sensor information fusion system(BP neural networks), the rate of identifying normal drilling (including normal removal of the drill bit from the working surface) was 82.2%, drill bit slippage was 75.6%, drilling through the bone tissue wall was 71.6% and cotton swab entanglement was 70.2%. The stop program made the drill stop drilling in 0.2-0.3 seconds when the drilling faults was identified.Under different conditions, the signal of each sensor changed consistently too, with high repeatability and regularity of signal variation,like the results of consistent conditions. The average identification rate was 81.3%,72.625%,68.575% and 70.5% respectively.ConclusionsThis study shows that, during normal drilling (including normal removal of the drill bit from the working surface), drill bit slippage, drilling through the bone tissue wall and cotton swab entanglement during otologic surgery, the forces acting on the drill bit change predictably under different conditions, characteristic signals can be extracted from three kinds of drilling faults. Using suitable BP neural networks, the drilling faults can be identified. This provides a good foundation of predicting the drilling faults and controlling the drill automatically. Further experiments are necessary to be done, these are underway.