A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.

Field testing of a remote controlled robotic tele-echo system in an ambulance using broadband mobile communication technology.

We report the testing of a mobile Robotic Tele-echo system that was placed in an ambulance and successfully transmitted clear real time echo imaging of a patient's abdomen to the destination hospital from where this device was being remotely operated. Two-way communication between the paramedics in this vehicle and a doctor standing by at the hospital was undertaken. The robot was equipped with an ultrasound probe which was remotely controlled by the clinician at the hospital and ultrasound images of the patient were transmitted wirelessly. The quality of the ultrasound images that were transmitted over the public mobile telephone networks and those transmitted over the Multimedia Wireless Access Network (a private networks) were compared. The transmission rate over the public networks and the private networks was approximately 256 Kbps, 3 Mbps respectively. Our results indicate that ultrasound images of far higher definition could be obtained through the private networks.