Machine Learning-Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air-Coupled Transducer

Automation in Dentistry with Mechanical Drills and Lasers for Implant Osteotomy: A Narrative-Scoping Review

The popularity of implants is increasing with the aging population requiring oral-dental rehabilitation. There are several critical steps in the implant workflow, including case selection, implant design, surgical procedure, biological tissue responses, and functional restoration. Among these steps, surgical osteotomy procedures are a crucial determinant of clinical success. This brief review was aimed at outlining the current state of the field in automation-assisted implant surgical osteotomy technologies. A broad search of the literature was performed to identify current literature. The results are outlined in three broad categories: semi-automated static (image-guided) or dynamic (navigation-assisted) systems, and fully-automated robotic systems. As well as the current mechanical rotary approaches, the literature supporting the use of lasers in further refinement of these approaches is reviewed. The advantages and limitations of adopting autonomous technologies in practical clinical dental practices are discussed. In summary, advances in clinical technologies enable improved precision and efficacious clinical outcomes with implant dentistry. Hard-tissue lasers offer further advancements in precision, improved biological responses, and favorable clinical outcomes that require further investigation.

Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography

This article presents a real-time noninvasive method for detecting bone and bone marrow in laser osteotomy. This is the first optical coherence tomography (OCT) implementation as an online feedback system for laser osteotomy. A deep-learning model has been trained to identify tissue types during laser ablation with a test accuracy of 96.28 %. For the hole ablation experiments, the average maximum depth of perforation and volume loss was 0.216 mm and 0.077 mm³, respectively. The contactless nature of OCT with the reported performance shows that it is becoming more feasible to utilize it as a real-time feedback system for laser osteotomy.

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

In Vitro and Ectopic In Vivo Studies toward the Utilization of Rapidly Isolated Human Nasal Chondrocytes for Single-Stage Arthroscopic Cartilage Regeneration Therapy

Nasal chondrocytes (NCs) have a higher and more reproducible chondrogenic capacity than articular chondrocytes, and the engineered cartilage tissue they generate in vitro has been demonstrated to be safe in clinical applications. Here, we aimed at determining the feasibility for a single-stage application of NCs for cartilage regeneration under minimally invasive settings. In particular, we assessed whether NCs isolated using a short collagenase digestion protocol retain their potential to proliferate and chondro-differentiate within an injectable, swiftly cross-linked and matrix-metalloproteinase (MMP)-degradable polyethylene glycol (PEG) gel enriched with human platelet lysate (hPL). NC-hPL-PEG gels were additionally tested for their capacity to generate cartilage tissue in vivo and to integrate into cartilage/bone compartments of human osteochondral plugs upon ectopic subcutaneous implantation into nude mice. NCs isolated with a rapid protocol and embedded in PEG gels with hPL at low cell density were capable of efficiently proliferating and of generating tissue rich in glycosaminoglycans and collagen II. NC-hPL-PEG gels developed into hyaline-like cartilage tissues upon ectopic in vivo implantation and integrated with surrounding native cartilage and bone tissues. The delivery of NCs in PEG gels containing hPL is a feasible strategy for cartilage repair and now requires further validation in orthotopic in vivo models.

Toward optoacoustic sciatic nerve detection using an all-fiber interferometric-based sensor for endoscopic smart laser surgery

Objectives

Laser surgery requires efficient tissue classification to reduce the probability of undesirable or unwanted tissue damage. This study aimed to investigate acoustic shock waves (ASWs) as a means of classifying sciatic nerve tissue.

Materials and Methods

In this study, we classified sciatic nerve tissue against other tissue types—hard bone, soft bone, fat, muscle, and skin extracted from two proximal and distal fresh porcine femurs—using the ASWs generated by a laser during ablation. A nanosecond frequency‐doubled Nd:YAG laser at 532 nm was used to create 10 craters on each tissue type's surface. We used a fiber‐coupled Fabry–Pérot sensor to measure the ASWs. The spectrum's amplitude from each ASW frequency band measured was used as input for principal component analysis (PCA). PCA was combined with an artificial neural network to classify the tissue types. A confusion matrix and receiver operating characteristic (ROC) analysis was used to calculate the accuracy of the testing‐data‐based scores from the sciatic nerve and the area under the ROC curve (AUC) with a 95% confidence‐level interval.

Results

Based on the confusion matrix and ROC analysis of the model's tissue classification results (leave‐one‐out cross‐validation), nerve tissue could be classified with an average accuracy rate and AUC result of 95.78  ± 1.3% and 99.58  ± 0.6%, respectively.

Conclusion

This study demonstrates the potential of using ASWs for remote classification of nerve and other tissue types. The technique can serve as the basis of a feedback control system to detect and preserve sciatic nerves in endoscopic laser surgery.