Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning

Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review

Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method's strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards.

Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement Tracking

OBJECTIVE

To develop a convolutional neural network-based computer vision model to recognize and track 2 mastoidectomy surgical instruments-the drill and the suction-irrigator-from intraoperative video recordings of mastoidectomies.

STUDY DESIGN

Technological development and model validation.

SETTING

Academic center.

METHODS

Ten 1-minute videos of mastoidectomies done for cochlear implantation by varying levels of resident surgeons were collected. For each video, containing 900 frames, an open-access computer vision annotation tool was used to annotate the drill and suction-irrigator class images with bounding boxes. A mastoidectomy instrument tracking module, which extracts the center coordinates of bounding boxes, was developed using a feature pyramid network and layered with DETECTRON, an open-access faster-region-based convolutional neural network. Eight videos were used to train the model, and 2 videos were used for testing. Outcome measures included Intersection over Union (IoU) ratio, accuracy, and average precision.

RESULTS

For an IoU of 0.5, the mean average precision for the drill was 99% and 86% for the suction-irrigator. The model proved capable of generating maps of drill and suction-irrigator stroke direction and distance for the entirety of each video.

CONCLUSIONS

This computer vision model can identify and track the drill and suction-irrigator from videos of intraoperative mastoidectomies performed by residents with excellent precision. It can now be employed to retrospectively study objective mastoidectomy measures of expert and resident surgeons, such as drill and suction-irrigator stroke concentration, economy of motion, speed, and coordination, setting the stage for characterization of objective expectations for safe and efficient mastoidectomies.

Applications of artificial intelligence-enabled robots and chatbots in ophthalmology: recent advances and future trends

PURPOSE OF REVIEW

Recent advances in artificial intelligence (AI), robotics, and chatbots have brought these technologies to the forefront of medicine, particularly ophthalmology. These technologies have been applied in diagnosis, prognosis, surgical operations, and patient-specific care in ophthalmology. It is thus both timely and pertinent to assess the existing landscape, recent advances, and trajectory of trends of AI, AI-enabled robots, and chatbots in ophthalmology.

RECENT FINDINGS

Some recent developments have integrated AI enabled robotics with diagnosis, and surgical procedures in ophthalmology. More recently, large language models (LLMs) like ChatGPT have shown promise in augmenting research capabilities and diagnosing ophthalmic diseases. These developments may portend a new era of doctor-patient-machine collaboration.

SUMMARY

Ophthalmology is undergoing a revolutionary change in research, clinical practice, and surgical interventions. Ophthalmic AI-enabled robotics and chatbot technologies based on LLMs are converging to create a new era of digital ophthalmology. Collectively, these developments portend a future in which conventional ophthalmic knowledge will be seamlessly integrated with AI to improve the patient experience and enhance therapeutic outcomes.

Chandelier-Assisted Scleral Buckling: A Literature Review

The treatment of retinal detachment (RD) has seen numerous advancements in the last decades. Scleral buckling (SB) is a surgical procedure introduced in the 1950s that has seen a drastic reduction with the advent of vitrectomy. However, due to the new surgical visualization systems, SB has evolved and continues to be an extremely useful procedure in certain conditions. The presence of different case reports or interventional studies with comparable outcomes, as well as the lack of recent studies with direct comparison, may result in an underestimation of its potential nowadays. The aim of this review is to provide a comprehensive update on chandelier-assisted scleral bucking (CSB), with an overview of the surgical evolution, outcomes, advantages, and complications.