Real-Time Monitoring with a Controlled Advancement Drill May Decrease Plunge Depth

Usefulness of a drill stopper to prevent iatrogenic soft tissue injury in orthopedic surgery

Objective

This study introduces a novel technique utilizing a drill stopper to limit drill penetration depth and to prevent iatrogenic injuries, specifically neurovascular damage, in orthopedic surgeries. Orthopedic surgeries frequently involve the use of drills, which are essential tools for various procedures. However, improper handling of drills can lead to iatrogenic soft tissue injuries, causing severe consequences such as permanent disability or life-threatening complications. To address this issue, we propose the use of a drill stopper as a safeguard to prevent excessive drill penetration and reduce the risk of soft tissue damage during surgery.

Materials and Methods

The study involved 32 orthopedic surgeons, half of whom were experienced and the other half inexperienced. Synthetic femur bone models (Synbone) were used for drilling exercises, employing four configurations: a sharp drill bit without a stopper (SF, Sharp Free), a sharp drill bit with a stopper (SS, Sharp Stopper), a blunt drill bit without a stopper (BF, Blunt Free), and a blunt drill bit with a stopper (BS, Blunt Stopper). Each participant conducted three trials for each configuration, and the penetration depth was measured after each trial.

Results

For experienced surgeons, the average penetration depths were 3.83 (±1.826)mm for SF, 11.02 (±3.461)mm for BF, 2.88 (±0.334)mm for SS, and 2.75 (±0.601)mm for BS. In contrast, inexperienced surgeons had average depths of 8.52 (±4.608)mm for SF, 18.75 (±4.305)mm for BF, 2.96 (±0.683)mm for SS, and 2.83 (±0.724)mm for BS.

Conclusion

The use of a drill stopper was highly effective in controlling drill penetration depth and preventing iatrogenic injuries during orthopedic surgeries. We recommend its incorporation, particularly when using a blunt drill bit or when an inexperienced surgeon operates in an anatomically unfamiliar area. Using the drill stopper, the risk of severe injuries from excessive drill penetration can be minimized, leading to improved patient safety and better surgical outcomes.

Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy

BACKGROUND

With advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy.

METHODS

Cervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states.

RESULTS

The amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT.

CONCLUSIONS

The KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.

Power-Tool Use in Orthopaedic Surgery: Iatrogenic Injury, Its Detection, and Technological Advances: A Systematic Review

Power tools are an integral part of orthopaedic surgery but have the capacity to cause iatrogenic injury. With this systematic review, we aimed to investigate the prevalence of iatrogenic injury due to the use of power tools in orthopaedic surgery and to discuss the current methods that can be used to reduce injury.

Motor Bur Milling State Identification via Fast Fourier Transform Analyzing Sound Signal in Cervical Spine Posterior Decompression Surgery

Objectives

To investigate the real‐time sensitive feedback parameter of the motor bur milling state in cervical spine posterior decompression surgery, to possibly improve the safety of cervical spine posterior decompression and robot‐assisted spinal surgeries.

Methods

In this study, the cervical spine of three healthy male and three healthy female pigs were randomly selected. Six porcine cervical spine specimens were fixed to the vibration isolation system. The milling state of the motor bur was defined as the lamina cancellous bone (CA), lamina ventral corticalbone (VCO), and penetrating ventral cortical bone (PVCO). A 5‐mm bur milled the CA and VCO, and a 2‐mm bur milled the VCO and PVCO. A miniature microphone was used to collect the sound signal (SS) of milling lamina which was then extracted using Fast Fourier Transform (FFT). When using 5‐mm and 2‐mm bur to mill, the CA, VCO, and PVCO of each specimen were continuously collected at 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 kHz frequencies for SS magnitudes. The study randomly selected the SS magnitudes of the CA and VCO continuously for 2 s at 1, 2, 3, 4, and 5 kHz frequencies for statistical analyses. When milling the VCO to the PVCO, we randomly collected the SS magnitudes of the VCO for consecutive 2 s and the SS magnitudes of continuous 2 s in the penetrating state at 1, 2, 3, 4, and 5 kHz frequencies for statistical analyses. The independent sample t‐test was used to compare the SS magnitudes of different milling states extracted from the FFT to determine the motor bur milling state.

Results

The SS magnitudes of the CA and VCO of all specimens extracted from the FFT at 1, 2, and 3 kHz were statistically different (P < 0.01); three specimens were not statistically different at a specific FFT‐extracted frequency (first specimen at 5 kHz, SS magnitudes of the CA were [25.94 ± 8.74] × 10⁻³, SS magnitudes of the VCO were [28.67 ± 12.94] × 10⁻³, P = 0.440; second specimen at 4 kHz, SS magnitudes of the CA were [23.79 ± 7.94] × 10⁻³, SS magnitudes of the VCO were [24.78 ± 4.32] × 10⁻³, P = 0.629; and third specimen at 5 kHz, SS magnitudes of the CA were [16.76 ± 6.20] × 10⁻³, SS magnitudes of the VCO were [17.69 ± 6.44] × 10⁻³, P = 0.643).The SS magnitudes of the VCO and PVCO of all the specimens extracted from the FFT at each frequency were statistically different (P < 0.001).

Conclusions

Based on the FFT extraction, the SS magnitudes of the motor bur milling state between the CA and VCO, the VCO and PVCO were significantly different, confirming that the SS is a potential sensitive feedback parameter for identifying the motor bur milling state. This study could improve the safety of cervical spine posterior decompression surgery, especially of robot‐assisted surgeries.