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BACKGROUND/AIMS: We investigated the clinical practice patterns of post-polypectomy colonoscopic surveillance among Korean endoscopists. METHODS: In a web-based survey conducted between September and November 2021, participants were asked about their preferred surveillance intervals and the patient age at which surveillance was discontinued. Adherence to the recent guidelines of the U.S. Multi-Society Task Force on Colorectal Cancer (USMSTF) was also analyzed. RESULTS: In total, 196 endoscopists completed the survey. The most preferred first surveillance intervals were: a 5-year interval after the removal of 1-2 tubular adenomas < 10 mm; a 3-year interval after the removal of 3-10 tubular adenomas < 10 mm, adenomas ≥ 10 mm, tubulovillous or villous adenomas, ≤ 20 hyperplastic polyps < 10 mm, 1-4 sessile serrated lesions (SSLs) < 10 mm, hyperplastic polyps or SSLs ≥ 10 mm, and traditional serrated adenomas; and a 1-year interval after the removal of adenomas with highgrade dysplasia, >10 adenomas, 5-10 SSLs, and SSLs with dysplasia. In piecemeal resections of large polyps ( > 20 mm), surveillance colonoscopy was mostly preferred after 1 year for adenomas and 6 months for SSLs. The mean USMSTF guideline adherence rate was 30.7%. The largest proportion of respondents (40.8%-55.1%) discontinued the surveillance at the patient age of 80-84 years. CONCLUSIONS: A significant discrepancy was observed between the preferred post-polypectomy surveillance intervals and recent international guidelines. Individualized measures are required to increase adherence to the guidelines.

Colonoscopic polypectomy is effective in decreasing the incidence and mortality of colorectal cancer (CRC). Premalignant polyps discovered during colonoscopy are associated with the risk of metachronous advanced neoplasia. Postpolypectomy surveillance is the most important method for managing advanced metachronous neoplasia. A more efficient and evidence-based guideline for postpolypectomy surveillance is required because of the limited medical resources and concerns regarding colonoscopy complications. In these consensus guidelines, an analytic approach was used to address all reliable evidence to interpret the predictors of CRC or advanced neoplasia during surveillance colonoscopy. The key recommendations state that the high-risk findings for metachronous CRC following polypectomy are as follows: adenoma ≥10 mm in size; 3 to 5 (or more) adenomas; tubulovillous or villous adenoma; adenoma containing high-grade dysplasia; traditional serrated adenoma; sessile serrated lesion containing any grade of dysplasia; serrated polyp of at least 10 mm in size; and 3 to 5 (or more) sessile serrated lesions. More studies are needed to fully comprehend the patients who are most likely to benefit from surveillance colonoscopy and the ideal surveillance interval to prevent metachronous CRC.

BACKGROUND/AIMS: The adenoma detection rate (ADR) does not reflect the complete detection of every adenoma during colonoscopy; thus, many surrogate indicators have been suggested. This study investigated whether the ADR and surrogate quality indicators reflect the adenoma miss rate (AMR) when performing qualified colonoscopy. METHODS: We performed a prospective, multicenter, cross-sectional study of asymptomatic examinees aged 50 to 75 years who underwent back-to-back screening colonoscopies by eight endoscopists. The ADR and surrogate quality indicators, including polyp detection rate, total number of adenomas per colonoscopy, additional adenomas found after the first adenoma per colonoscopy (ADR-Plus), and total number of adenomas per positive participant, were calculated for the prediction of AMR. RESULTS: A total of 371 back-to-back colonoscopies were performed. There was a significant difference in ADRs (range, 44% to 75.4%; p=0.024), polyp detection rates (range, 56% to 86.9%; p=0.008) and adenomas per positive participants (range, 1.19 to 2.30; p=0.038), and a tendency of a difference in adenomas per colonoscopy (range, 0.62 to 1.31; p=0.051) and ADR-Plus (range, 0.13 to 0.70; p=0.054) among the endoscopists. The overall AMR was 20.1%, and AMRs were not different (range, 13.9 to 28.6; p>0.05) among the endoscopists. No quality indicators were significantly correlated with AMR. The number of adenomas found during the first colonoscopy was an independent factor for increased AMR (odds ratio, 1.79; p

PURPOSE: Quality indicators of the National Endoscopy Quality Improvement Program (NEQIP) and outcome measures of endoscopy in the National Cancer Screening Program (NCSP) in Korea are not clear. We evaluated the quality indicators of the revised NEQIP and outcome measures of endoscopy at different types of healthcare facilities participating in the NCSP. MATERIALS AND METHODS: This study was conducted between March and August 2018 in primary, secondary, and tertiary healthcare facilities that perform endoscopy as a part of the NCSP. Representative endoscopists completed a questionnaire for quality indicators of the NEQIP and provided data on outcome measures for endoscopy. RESULTS: Quality indicators of the NEQIP were mostly acceptable. However, the quality indicators for annual volume of esophagogastroduodenoscopy (EGD) and colonoscopy, training for endoscopy quality improvement by endoscopy nursing staff, colonoscopy reports, documentation of pathologic lesions, quality of endoscopy reprocessing areas, and completion of endoscopy reprocessing education programs were suboptimal. For outcome measures of EGD, the number of photo-documentations and total procedure time were higher at tertiary healthcare facilities than at other facilities (p

Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, a low-power and lightweight design of the HAR system is essential. In this paper, we propose a low-power and lightweight HAR system using point-cloud data collected by radar. The proposed HAR system uses a pillar feature encoder that converts 3D point-cloud data into a 2D image and a classification network based on depth-wise separable convolution for lightweighting. The proposed classification network achieved an accuracy of 95.54%, with 25.77 M multiply–accumulate operations and 22.28 K network parameters implemented in a 32 bit floating-point format. This network achieved 94.79% accuracy with 4 bit quantization, which reduced memory usage to 12.5% compared to existing 32 bit format networks. In addition, we implemented a lightweight HAR system optimized for low-power design on a heterogeneous computing platform, a Zynq UltraScale+ ZCU104 device, through hardware–software implementation. It took 2.43 ms of execution time to perform one frame of HAR on the device and the system consumed 3.479 W of power when running. [ABSTRACT FROM AUTHOR]

The range resolution of FMCW radar is critical for accurately distinguishing between multiple objects. Higher range resolution allows for better object separation and more precise location determination. While increasing the bandwidth can improve range resolution, it also raises costs and may be subject to regulatory constraints on the available frequency spectrum. This paper proposes an approach to enhance range resolution by increasing the effective bandwidth through signal processing, specifically by introducing the Estimated Signal Padding (ESP) method. ESP extends the bandwidth by lengthening the signal in the time domain, creating a synthesized signal for each target. The proposed algorithm can improve range resolution by a factor of at least 2.25. Unlike existing methods such as Zero Padding and Neural Network Padding, the proposed algorithm can distinguish targets of varying sizes rather than only targets of the same size. The performance of the algorithm has been validated through MATLAB simulations and FMCW radar experiments. [ABSTRACT FROM AUTHOR]

Colonoscopy has a limited field of view because it relies solely on a small camera attached to the end of the scope and a screen displayed on a monitor. Consequently, the quality and safety of diagnosis and treatment depend on the experience and skills of the gastroenterologist. When a novice attempts to insert the colonoscope during the procedure, excessive pressure can sometimes be applied to the colon wall. This pressure can cause a medical accident known as colonic perforation, which the physician should prevent. We propose an assisting device that senses the pressure applied to the colon wall, analyzes the risk of perforation, and warns the physician in real time. Flexible pressure sensors are attached to the surface of the colonoscope shaft. These sensors measure pressure signals during a colonoscopy procedure. A simple signal processor is used to collect and process the pressure signals. In the experiment, a colonoscope equipped with the proposed device was inserted into a simulated colon made from a colon extracted from a pig. The processed data were visually communicated to the gastroenterologist via displays and light-emitting diodes (LEDs). The device helps the physician continuously monitor and prevent excessive pressure on the colon wall. In this experiment, the device appropriately generated and delivered warnings to help the physicians prevent colonic perforation. In the future, the device is to be improved, and more experiments will be performed in live swine models or humans to confirm its efficacy and safety. [ABSTRACT FROM AUTHOR]

The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations in RDA necessitates high bandwidth and lacks data reuse, leading to bottlenecks. This paper introduces a synthetic aperture radar dual in-line memory module (SARDIMM), which executes RDA operations near memory via near-memory processing (NMP), thereby effectively reducing memory accesses, execution time, and energy consumption. The embedded NMP module in SARDIMM optionally supports a combination of FFT, IFFT, and matched filter operations of the RDA for range and azimuth compression. The operator within the NMP module accelerates the FFT by performing two radix-2 single butterfly operations in parallel. The NMP module was implemented and validated on a Xilinx UltraScale+ field-programmable gate array (FPGA) using Verilog-HDL. The acceleration performance of RDA for images of various sizes was evaluated through a simulator modified with gem5 and DRAMSim3 and achieved a 6.34–6.93× speedup and 41.9–48.2% energy savings. [ABSTRACT FROM AUTHOR]

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms. [ABSTRACT FROM AUTHOR]

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. [ABSTRACT FROM AUTHOR]

Summary: Background/Aim: We aimed to validate clinical decision support tools (CDSTs) to predict real‐life effectiveness of vedolizumab (VDZ) in patients with inflammatory bowel disease. Methods: We retrospectively enrolled patients with Crohn's disease (CD) or ulcerative colitis (UC) treated with VDZ at 10 tertiary referral centres in Korea between January 2017 and November 2021. We assessed clinical remission (CREM) and response (CRES), corticosteroid‐free clinical remission (CSF‐CREM) and response (CSF‐CRES), biochemical response based on C‐reactive protein (BioRES[CRP]) and faecal calprotectin (BioRES[FC]), endoscopic healing (EH), and the need to optimise or switch drugs based on CDST‐defined response groups. Additionally, the area under the receiver operating characteristics curve (AUC) for the CDSTs was calculated. Results: We included 143 patients with CD and 219 with UC. We observed incremental trends on CSF‐CRES at week 14 (W14) (ptrend = 0.004) and decreasing trends for the need to optimise or switch drugs (ptrend = 0.016) in CD from the low to high probability groups. Except for CSF‐CREM at W54, we noticed incremental trends for all clinical responses at W14, W26 and W54 (ptrend <0.001) in UC. W26 and W54 BioRES[CRP] and W14 EH also showed increasing trends (ptrend <0.05) in UC. With increasing probabilities of response, drug optimisation or switching was less frequently required in UC (ptrend = 0.013). With 26 points cut‐off, CDSTs effectively identified W14 CSF‐CRES, W26 BioRES[CRP], BioRES[FC] and W54 BioRES[CRP] in UC, all with AUCs >0.600, whereas CDSTs showed poor accuracy in CD. Conclusions: CDSTs for VDZ had acceptable accuracy in predicting effectiveness outcomes including clinical and biochemical outcomes in UC. However, their utility in CD was limited. [ABSTRACT FROM AUTHOR]