Author: hxwu

Port wine stains (PWS) are congenital vascular malformations that can lead to significant psychological and physical complications if untreated. Photodynamic therapy (PDT) is a common treatment but shows variable efficacy due to the heterogeneous vascular architecture of PWS lesions. Current diagnostic methods relying on skin surface appearance often fail to reflect underlying structural differences of PWS lesions, leading to erratic treatment effects.

Optical coherence tomography (#OCT) and OCT angiography (#OCTA) are promising tools for imaging PWS lesions. However, existing OCTA quantitative metrics cannot show significant differences among the various PWS subtypes based on clinical skin appearance diagnosis. This work proposes a fine-grained classification method for PWS using OCT and OCTA features. Compared with current clinical diagnosis based on skin appearance, the proposed method reveals the angiopathological heterogeneity of hypodermic PWS lesions and has the potential to provide more effective subtyping and treatment strategies for PWS.

Research team: Xiaofeng Deng, Defu Chen, Bowen Liu, Xiwan Zhang, Haixia Qiu, Wu ‘Scott’ YUAN, and Hongliang Ren.

Paper available at: 10.1109/JBHI.2025.3545931

Our paper entitled “Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-driven Surface Normal-aware Tracking and Mapping” has been accepted at #ICRA2025! 🎉

Multi-view inconsistencies in 3D Gaussian Splatting (3DGS) have long limited precise depth and surface reconstruction in minimally invasive surgery. Our team (Yiming Huang *, Beilei Cui *, Long Bai *, Zhen Chen, Jinlin Wu, Zhen Li, Hongbin Liu, Hongliang Ren) from The Chinese University of Hong Kong and Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS introduces Endo-2DTAM—a real-time endoscopic SLAM system that combines 2DGS with a surface normal-aware pipeline to achieve geometrically accurate, high-quality reconstruction.

🔑 Key Innovations:

✅ Robust tracking via point-to-point/plane metrics

✅ Surface enhancement using normal consistency & depth distortion

✅ Pose-consistent keyframe sampling for coherence

📈 Results:

●    1.87±0.63 mm RMSE in depth reconstruction

●    Real-time rendering & efficient computation

●    Open-source code: GitHub (https://lnkd.in/gyRr2YxS)

Paving the way for safer, smarter surgical robotics! 🤖💡

This recognition highlights Prof. Ren’s contributions to intelligent medical robotics and AI. Join us in congratulating Prof. Ren on this well-deserved achievement! 🎉👏

🔗 Check the awards inauguration ceremony at https://lnkd.in/gEAcPecJ

– Background

Open tracheostomy (OT) remains the gold standard for managing airway obstruction; however, it is associated with stringent procedural requirements, scarring, and risks of infection. Percutaneous dilation tracheostomy (PDT), while more cost-effective, less invasive, and safer for surgeons, carries the risk of injuring the posterior tracheal wall and esophagus. Additionally, precise identification of tracheal rings and the optimal puncture site can be challenging.

– Our Contribution

To address these limitations and enhance the safety and simplicity of tracheostomy procedures, a minimally invasive, endotracheal inside-out flexible needle-driving system has been developed for microendoscope-guided robotic tracheostomy (MERT). This system integrates optical coherence tomography (OCT) and microendoscopic guidance to facilitate robotic insertion into the trachea, enabling an inside-out puncture with a flexible needle. The device is designed to operate through a standard endotracheal tube (ETT), and the puncture direction of the flexible needle is fully adjustable. Kinematic and static models of the flexible needle have been developed, and the system’s feasibility has been validated through porcine trachea puncture experiments.

This approach demonstrates significant potential for improving tracheostomy outcomes by minimizing invasiveness and enhancing procedural precision.

– Authors: Botao Lin, Sishen YUAN, Tinghua Zhang, Tao Zhang, Ruoyi Hao, Wu ‘Scott’ YUAN, Chwee Ming Lim, and Hongliang Ren

Looking forward to sharing our findings with the robotics and medical communities at #ICRA2025! 🌐🤖🩺

This work introduces a modular robotic platform designed to assist in nasotracheal intubation, a critical yet challenging procedure in clinical settings. While orotracheal intubation robots have seen significant development, nasotracheal intubation remains underdeveloped, particularly in terms of safety mechanisms. Our robot features a variable-stiffness fiberoptic bronchoscope (FOB) control module, enabling both low-stiffness mode for safe navigation through the nasal cavity and high-stiffness mode for enhanced load-bearing near the glottis. Additionally, a compact FOB feeding module with passive failure protection ensures controlled force application, minimizing risks during the procedure.

We’re grateful to our team (Ruoyi Hao, Sam, Jiewen Lai, Wenqi Zhong, Dihong Xie, Yu Tian, Tao Zhang, Zhang Yang, Catherine P. L. Chan, Jason Y. K. Chan, and Hongliang Ren) for their dedication and to #ICRA for this opportunity to share our work.

Looking forward to discussing this with the robotics and medical communities!

In this work, we introduce the Tangent Line Decomposition (TLD) algorithm, a new approach to finding collision-free paths in 2D polygon and 3D polyhedron environments.

TLD simplifies path planning by breaking it into smaller steps, focusing on one key obstacle at a time. Instead of building a complete graph, it uses a best-first search to reduce unnecessary computations. While the paths generated by TLD may not always be optimal, they can serve as a helpful starting point for other algorithms to refine further.

In our experiments, TLD showed improvements over the baseline LTA* method, achieving faster planning speeds in both 2D and 3D environments. The approach is also flexible, working in both convex and concave obstacle settings when combined with convex decomposition.

We look forward to sharing more details at ICRA 2025 and learning from others in the field!

In this post, we are pleased to share a paper entitled “Three-dimension Tip Force Perception and Axial Contact Location Identification for Flexible Endoscopy using Tissue-compliant Soft Distal Attachment Cap Sensors”, contributed by Zhang T†, Yang Yang†, Yang Yang, Huxin Gao, Sam, Jiewen Lai and Hongliang Ren∗.

In endoluminal surgeries, inserting a flexible endoscope is one of the fundamental procedures. During this process, vision remains the primary feedback, while the perception of tactile magnitude and location is insufficient.

To address this issue, we propose a fiber Bragg grating (FBG)–based tissue-compliant sensor cap with multi-mode sensing capabilities, including contact location identification at the terminal surface and the three-dimensional contact force perception at the tip. Utilizing the relative contact location information, operators can adjust the steerable segment of the endoscope when transitioning from one segment of a natural orifice to a narrower segment, which may be obstructed by constricted lumens(Fig.1).

The FBG-based sensor can perceive the tip contact force and identify the axial contact location with high precision, which is shown in Fig.2. The experimental results demonstrate the potential of the proposed sensing mechanism to be applied in surgeries requiring endoscope insertions.

Stay tuned for more research from our lab on endoscopic force sensing!

🎉 Congrates to WENCHAO YUE, Yunong, and all the collaborators!

Here are some highlights:

1. Our lab members Sishen YUAN, Tao Zhang, YIMING HUANG, and Yupeng Wang presented 6 papers covering medical robotics, augmented reality, and surgical scene rendering, among others.

2. One of our accepted papers, entitled “Multimodal Augmented Reality Assisted Incision Guidance for Preoperative Tracheostomy Planning” was awarded the Finalist of Best Student Paper Award!

3. Prof. Hongliang Ren gave a talk on “Compliant Endoscopic Motion Generation and Perception towards Intelligent Minimally Invasive Robotic Procedures” at the Workshop on Actuation, Control, Fabrication, and Application of Miniature Robots in Biomedical Engineering.

We are grateful for the opportunity to share our work and look forward to continuing our research in medical robotics and AI.

During the 2024 International Automatic Control Conference (CACS 2024, https://lnkd.in/gYVg4tx5), Prof. Hongliang Ren was invited as a keynote speaker and delivered a talk on “Compliant Robotic Motion Control and Perception Towards Intelligent Minimally Invasive Surgical Procedures”. The conference provided a great platform for sharing our latest research and connecting with fellow experts in the field.