Latest AI and machine learning research in surgery for healthcare professionals.
Purpose: Foundation models, trained on multitudes of public datasets, often require additional fine-tuning or re-prompting mechanisms to be applied to visually distinct target domains such as surgical videos. Further, without domain knowledge, they cannot model the specific semantics of the target domain. Hence, when applied to surgical video segmentation, they fail to generalise to sections whe...
Automated analysis of surgical videos is crucial for improving surgical training, workflow optimization, and postoperative assessment. We introduce a CSMAE, Masked Autoencoder (MAE)-based pretraining approach, specifically developed for Cataract Surgery video analysis, where instead of randomly selecting tokens for masking, they are selected based on the spatiotemporal importance of the token. W...
Surgical simulation offers a promising addition to conventional surgical training. However, available simulation tools lack photorealism and rely on...
Prostate cancer is a leading health concern among men, requiring accurate and accessible methods for early detection and risk stratification. Prosta...
This study aims to advance surgical phase recognition in arthroscopic procedures, specifically Anterior Cruciate Ligament (ACL) reconstruction, by i...
Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in s...
Training large language models (LLMs) typically relies on adaptive optimizers like Adam (Kingma & Ba, 2015) which store additional state information...
Indexing endoscopic surgical videos is vital in surgical data science, forming the basis for systematic retrospective analysis and clinical performa...
In laparoscopy surgical training and evaluation, real-time detection of surgical actions with interpretable outputs is crucial for automated and rea...
Recent deep learning-based image completion methods, including both inpainting and outpainting, have demonstrated promising results in restoring cor...
Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precu...
Medical ultrasound has been widely used to examine vascular structure in modern clinical practice. However, traditional ultrasound examination often...
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subse...
Referral workflow inefficiencies, including misaligned referrals and delays, contribute to suboptimal patient outcomes and higher healthcare costs. ...
Self-supervised learning has revolutionized medical imaging by enabling efficient and generalizable feature extraction from large-scale unlabeled da...
This project aims to compare various language classification procedures, procedures combining various Python language detection algorithms and metad...
Depth estimation from monocular endoscopic images presents significant challenges due to the complexity of endoscopic surgery, such as irregular sha...
Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a cr...
The integration of language instructions with robotic control, particularly through Vision Language Action (VLA) models, has shown significant poten...
Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic mani...