Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation.

Background and objective: Medical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as "wrong level surgery". However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images. Methods: We proposed an integrated system with three key modules: hand landmark detection, hand pointing, and hand gesture recognition. A proposed depth enhancement algorithm is combined with a deep learning hand landmark detector to generate hand landmarks. Based on the designed system, a proposed hand-pointing system combined with projection and ray-pointing techniques allows for reducing fatigue during manipulation. A proposed landmark geometry constraint algorithm and deep learning method were applied to detect six gestures including click, open, close, zoom, drag, and rotation. Additionally, a control menu was developed to effectively activate common functions. Results: The proposed hand-pointing system allowed for a large control range of up to 1200 mm in both vertical and horizontal direction. The proposed hand gesture recognition method showed high accuracy of over 97% and real-time response. Conclusion: This paper described the contagious infection-free medical interaction system that enables precise and effective manipulation of medical images within the large control range, while minimizing hand fatigue.

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

Impact of the Supra-papilla Method on Patency in Distal Malignant Biliary Obstruction: A Multicenter Randomized Controlled Trial.

BACKGROUND: /aims: Placement of a self-expandable metal stent (SEMS) across the duodenal major papilla carries a risk of duodenobiliary reflux (DBR). The supra-papilla method of stenting may reduce DBR and improve stent patency compared to the trans-papilla method. This study aimed to compare the clinical outcomes between the supra-papilla and trans-papilla methods for distal malignant biliary obstruction (DMBO). METHODS: Between January 2021 and January 2023, consecutive patients with DMBO from six centers in Korea were randomly assigned to either the supra-papilla or trans-papilla method arm in a 1:1 ratio. The primary outcome was the duration of stent patency, and secondary outcomes included the cause of stent dysfunction, adverse events, and overall survival rate. RESULTS: A total of 84 patients were equally assigned to each group. The most common cause of DMBO was pancreatic cancer (n=50, 59.5%), followed by bile duct (n=20, 23.8%), gallbladder (n=11, 13.1%), and other cancers (n=3, 3.6%). Stent patency was significantly longer in the supra-papilla group (median [95% confidence interval], 369 [289-497] vs. 154 [78-361] days; P < 0.01). Development of DBR was significantly lower in the supra-papilla group (9.4% vs 40.8%, P < 0.01). Adverse events and overall survival rate were not significantly different between the two groups. CONCLUSIONS: The placement of SEMS using the supra-papilla method resulted in a significantly longer duration of stent patency. It is advisable to place the SEMS using the supra-papilla method in DMBO. Further studies with a larger number of patients are required to validate the benefits of the supra-papilla method.

A Case of Biliary Cast Syndrome with Cholangiocarcinoma-like Lesion in a Patient with No History of Liver Transplantation.

Background and Objectives: Biliary cast syndrome, which was first reported in 1975, is a rare disease that occurs after liver transplantation. The incidence is even lower in patients who have not undergone liver transplantation. This study reports a rare case of biliary cast syndrome with cholangiocarcinoma-like lesions in a patient who did not undergo liver transplantation. Case Report: Herein, we report a case of a 69-year-old man with right upper quadrant pain and elevated levels of alkaline phosphatase and gamma-glutamyl transferase, who had a history of total gastrectomy for gastric cancer and laparoscopic cholecystectomy for acute cholecystitis. Computed tomography (CT) revealed longitudinal bile stones in the extrahepatic and intrahepatic bile ducts and abrupt narrowing of the left main bile duct accompanied by a narrowing of the upstream bile duct in the left lobe of the liver. Based on the CT findings, the removal of the bile stones in the bile duct and additional examinations of the suspected cholangiocarcinoma were performed. The patient's symptoms improved, and examinations for suspected cholangiocarcinoma showed no abnormal findings, and he was discharged one month later. Conclusions: The purpose of this case report is to share a rare case of Biliary Cast Syndrome (BCS) occurring without liver transplantation. Additionally, the report aims to share image findings that mimic cancer in BCS, with the goal of reducing unnecessary repetitive biopsies, minimizing patient discomfort, and decreasing unnecessary costs by aiding in the diagnosis of BCS.

A multi-level capacitor-less memory cell fabricated on a nano-scale strained silicon-on-insulator.

A multi-level capacitor-less memory cell was fabricated with a fully depleted n-metal-oxide-semiconductor field-effect transistor on a nano-scale strained silicon channel on insulator (FD sSOI n-MOSFET). The 0.73% biaxial tensile strain in the silicon channel of the FD sSOI n-MOSFET enhanced the effective electron mobility to ∼ 1.7 times that with an unstrained silicon channel. This thereby enables both front- and back-gate cell operations, demonstrating eight-level volatile memory-cell operation with a 1 ms retention time and 12 µA memory margin. This is a step toward achieving a terabit volatile memory cell.