George Mylonas

Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik et al.

The operating room (OR) is a dynamic and complex environment consisting of a multidisciplinary team working together in a high take environment to provide safe and efficient patient care. Additionally, surgeons are frequently exposed to multiple psycho-organisational stressors that may cause negative repercussions on their immediate technical performance and long-term health. Many factors can therefore contribute to increasing the Cognitive Workload (CWL) such as temporal pressures, unfamiliar anatomy or distractions in the OR. In this paper, a cascade of two machine learning approaches is suggested for the multimodal recognition of CWL in four different surgical task conditions. Firstly, a model based on the concept of transfer learning is used to identify if a surgeon is experiencing any CWL. Secondly, a Convolutional Neural Network (CNN) uses this information to identify different degrees of CWL associated to each surgical task. The suggested multimodal approach considers adjacent signals from electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS) and eye pupil diameter. The concatenation of signals allows complex correlations in terms of time (temporal) and channel location (spatial). Data collection was performed by a Multi-sensing AI Environment for Surgical Task & Role Optimisation platform (MAESTRO) developed at the Hamlyn Centre, Imperial College London. To compare the performance of the proposed methodology, a number of state-of-art machine learning techniques have been implemented. The tests show that the proposed model has a precision of 93%.

Tochukwu Onyeogulu, Salman Khan, Izzeddin Teeti et al.

Nowadays, there are more surgical procedures that are being performed using minimally invasive surgery (MIS). This is due to its many benefits, such as minimal post-operative problems, less bleeding, minor scarring, and a speedy recovery. However, the MIS's constrained field of view, small operating room, and indirect viewing of the operating scene could lead to surgical tools colliding and potentially harming human organs or tissues. Therefore, MIS problems can be considerably reduced, and surgical procedure accuracy and success rates can be increased by using an endoscopic video feed to detect and monitor surgical instruments in real-time. In this paper, a set of improvements made to the YOLOV5 object detector to enhance the detection of surgical instruments was investigated, analyzed, and evaluated. In doing this, we performed performance-based ablation studies, explored the impact of altering the YOLOv5 model's backbone, neck, and anchor structural elements, and annotated a unique endoscope dataset. Additionally, we compared the effectiveness of our ablation investigations with that of four additional SOTA object detectors (YOLOv7, YOLOR, Scaled-YOLOv4 and YOLOv3-SPP). Except for YOLOv3-SPP, which had the same model performance of 98.3% in mAP and a similar inference speed, all of our benchmark models, including the original YOLOv5, were surpassed by our top refined model in experiments using our fresh endoscope dataset.

Junhong Chen, Ziqi Yang, Haoyuan G Xu et al.

Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications.

Kiyoteru Miyashita, Timo Oude Vrielink, George Mylonas

This paper introduces a new surgical end-effector probe, which allows to accurately apply a contact force on a tissue, while at the same time allowing for high resolution and highly repeatable probe movement. These are achieved by implementing a cable-driven parallel manipulator arrangement, which is deployed at the distal-end of a robotic instrument. The combination of the offered qualities can be advantageous in several ways, with possible applications including: large area endomicroscopy and multi-spectral imaging, micro-surgery, tissue palpation, safe energy-based and conventional tissue resection. To demonstrate the concept and its adaptability, the probe is integrated with a modified da Vinci robot instrument.