Making Robots Mill Bone More Like Human Surgeons: Using Bone Density and Anatomic Information to Mill Safely and Efficiently

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.

A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches

BACKGROUND

From traditional open surgery to laparoscopic surgery and robot-assisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.

METHODS

A PRISMA-guided search was conducted across PubMed and OVID.

RESULTS

Of the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intra-operative registration, trajectory drilling, and soft tissue robotic surgery were discussed.

CONCLUSION

Although automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.

Perspective review on applications of optics in skull base surgery

The use of optic technology in skull base surgeries has the potential to revolutionize the field of medicine, particularly neurosurgery and neurology. Here, we briefly present the past, present, and future of skull-base surgery, with an emphasis on the applications of optical topography techniques. We discuss optical topography techniques such as functional near-infrared spectroscopy, optical diffusion tomography, and optical topographical imaging. Optical topography techniques are particularly advantageous when combined with other imaging methods. For instance, optical topography can be combined with techniques such as functional magnetic resonance imaging (fMRI) to combine the temporal resolution of optical topography with the spatial resolution of fMRI. Multimodal approaches will be critical to advance brain-related research as well as medicine. Structured light imaging techniques are also writing the future of 3-dimensional imaging. In short, optical topography can allow for non-invasive, high-resolution imaging that will provide real-time visualizations of the brain that are ideal for neurosurgery. From the limitations of traditional skull base surgeries to the newest developments in optical neuroimaging, here we will discuss the potential applications of optics in skull base procedures.

Human-Inspired Haptic Perception and Control in Robot-Assisted Milling Surgery

Bone milling is one of the most widely used and high-risk procedures in various types of surgeries, and it is important to be noted that the experienced surgeon can perform such an operation safely. The objective of this article is to enhance the safety of the robot-assisted milling operation with the inspiration of human haptic perception. The emergence, coding and perception of the human haptic are introduced. Following this, a single axis accelerometer that measures the vibration of the surgical power tool is mounted in the robot arm, and the recorded acceleration signal is encoded as parallel stream of binary data. The data are subsequently inputted to the Hopfield network so as to identify the milling state. Inspired by human inference procedure, the fuzzy logic controller is introduced to control the robot to track the desired state when performing bone milling operations. A real-time implementation of the proposed method on a digital signal processing is also described. The experimental results in milling porcine spines prove that the robot accurately discriminates different milling states even when the additive noise is serious, and the safe motion control of the robot is also realized.

Computational Optimization of Notch Spacing for a Transnasal Ear Endoscopy Continuum Robot

This paper presents a computational framework to optimize the visual coverage attainable by a notched-tube continuum robotic endoscope inside the middle ear cavity. Our framework combines anatomically-accurate geometric (mesh) models of the middle ear with a sampling-based motion planning algorithm (RRT) and a ray-casting procedure to quantify what regions of the middle ear can be accessed and visualized by the endoscope. To demonstrate the use of this framework, we run computer simulations to investigate the effect of varying the distance between each pair of consecutive flexure elements (i.e., notches) in our robotic endoscope.

Outlook and future of inner ear therapy

Drug delivery to the inner ear is an ideal method to treat a wide variety of otologic conditions. A broad range of potential applications is just beginning to be explored. New approaches combine principles of inner ear pharmacokinetics with emerging technologies of drug delivery including novel delivery systems, drug-device combinations, and new categories of drugs. Strategies include cell-specific targeting, manipulation of gene expression, local activation following systemic delivery, and use of stem cells, viral vectors, and gene editing systems. Translation of these therapies to the clinic remains challenging given the potential risks of intracochlear and intralabyrinthine trauma, our limited understanding of the etiologies of particular inner ear disorders, and paucity of accurate diagnostic tools at the cellular level. This review provides an overview of future methods, delivery systems, disease targets, and clinical considerations required for translation to clinical medicine.