PMS Quantitative Analysis Workbench
Advanced intelligent data integration and analysis capabilities help improve clinical efficiency and treatment results
NMPA class II certificate approved
Wukong Image Acquisition Workbench
Our portable medical device offers radiation-free 3D light analysis of the body, allowing for accurate assessment of spinal morphology
4DOrtho service
Intelligent orthotic design with personalized adjustments targeting the progression of spinal deformity and mechanical optimization
NMPA class I certificate approved
AlignPro Expert Studio
Provide users with convenient, efficient, and personalized musculoskeletal training and disease management solutions
About Us
CoNova Medical Technology
CoNova Medical Technology Co., Ltd. is a provider of digital-intelligent medical health management solutions. The company was co-founded by researchers from the Digital-Intelligent Health Laboratory at the University of Hong Kong's Faculty of Medicine and engineers from the intelligent technology industry. The company is dedicated to providing digital-intelligent medical health management solutions. The founding team includes clinical surgery experts from the University of Hong Kong's Faculty of Medicine, as well as AI scientists and engineers from the Department of Computer Science at the University of Cambridge and Silicon Valley.CoNova Medical Technology is committed to evidence-based medicine and digital-intelligent innovation, enabling high-level intelligent orthopedic diagnosis and treatment services. The company aims to enhance the 'intelligent ability' of medical staff, improve the 'intelligent service' for patients, and optimize the 'intelligent management' for medical institutions, all in an effort to support the department's transition towards building intelligent orthopedics.
Intelligent Management
Intelligent Service
Intelligent Ability
About Us
CoNova Medical Technology
CoNova Medical Technology Co., Ltd. is a provider of digital-intelligent medical health management solutions. The company was co-founded by researchers from the Digital-Intelligent Health Laboratory at the University of Hong Kong's Faculty of Medicine and engineers from the intelligent technology industry. The company is dedicated to providing digital-intelligent medical health management solutions. The founding team includes clinical surgery experts from the University of Hong Kong's Faculty of Medicine, as well as AI scientists and engineers from the Department of Computer Science at the University of Cambridge and Silicon Valley.CoNova Medical Technology is committed to evidence-based medicine and digital-intelligent innovation, enabling high-level intelligent orthopedic diagnosis and treatment services. The company aims to enhance the 'intelligent ability' of medical staff, improve the 'intelligent service' for patients, and optimize the 'intelligent management' for medical institutions, all in an effort to support the department's transition towards building intelligent orthopedics.Intelligent Management
Intelligent Service
Intelligent Ability
PMS Quantitative Analysis Workbench
Wukong Image Acquisition Workbench
AlignPro Expert Studio
4DOrtho service
PMS Quantitative Analysis Workbench
Wukong Image Acquisition Workbench
AlignPro Expert Studio
4DOrtho serviceResearch Projects
Medical Image Registration And Navigation
In this project, we aim to investigate and develop novel image registration techniques for medical imaging analysis. We explore different registration methods, including intensity-based, feature-based, and learning-based, and evaluate their performance on various medical imaging modalities, such as CT, MR, optical imagery etc.
Orthopaedic Biomechanics and Implant Design
We have demonstrated that trabecular microarchitecture (TraMicroArcht) can vary the apparent modulus (E), yield strength of trabecular bone accurately. Micro-finite element analysis can quantify the effects of TraMicroArcht on mechanical properties. MicroCT images can be converted directly into digital models via our in-house developed new system and simulate virtual loading effects. Our new system can also have significant application in fast implant design and optimization.
3D Quantitative Analysis & Surgical Planning
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
Multi-View Mark-Less Surgical Navigation System
Advancements in spinal surgery have been made with the use of screw-based fixation devices. To enhance the precision of interventions such as screw insertion, we would propose a multi-view mark-less surgical navigation system. This system provides real-time tracking of surgical instruments and accurate registration of preoperative CT images. The benefits include improved accuracy, reduced invasiveness, decreased radiation exposure, and potentially more effective procedures.
Research Projects
Medical Image Registration And Navigation
In this project, we aim to investigate and develop novel image registration techniques for medical imaging analysis. We explore different registration methods, including intensity-based, feature-based, and learning-based, and evaluate their performance on various medical imaging modalities, such as CT, MR, optical imagery etc.
Orthopaedic Biomechanics and Implant Design
We have demonstrated that trabecular microarchitecture (TraMicroArcht) can vary the apparent modulus (E), yield strength of trabecular bone accurately. Micro-finite element analysis can quantify the effects of TraMicroArcht on mechanical properties. MicroCT images can be converted directly into digital models via our in-house developed new system and simulate virtual loading effects. Our new system can also have significant application in fast implant design and optimization.
3D Quantitative Analysis & Surgical Planning
We have developed an AI-driven pipeline for the quantitative analysis of the spine. We have established an unsupervised deep learning framework called MRI-SegFlow for the segmentation of multiple spinal tissues in spinal MRI, which achieved comparable performance with the state-of-art supervised methods and without relying on any manually masked ground truth.
Multi-View Mark-Less Surgical Navigation System
Advancements in spinal surgery have been made with the use of screw-based fixation devices. To enhance the precision of interventions such as screw insertion, we would propose a multi-view mark-less surgical navigation system. This system provides real-time tracking of surgical instruments and accurate registration of preoperative CT images. The benefits include improved accuracy, reduced invasiveness, decreased radiation exposure, and potentially more effective procedures.