Missed opportunities to screen for Barrett's esophagus in the primary care setting of a large health system.

BACKGROUND AND AIMS: The rate of esophageal adenocarcinoma (EAC) is rising. This is partly due to the lack of identification of Barrett's esophagus (BE), the main risk factor for EAC. Identifying neoplastic BE can allow for endoscopic therapy to prevent EAC. Our aim was to determine how many patients eligible for screening are actually being screened for BE in the primary care setting of a large health system. METHODS: A digital search algorithm was constructed using the established gastroenterology guidelines and the Kunzmann model for screening for BE. The algorithm was then applied to the electronic medical record of all patients seen in the primary care setting of the health system. A manual review of charts of the identified patients was performed to confirm the high-risk status and determine if screening occurred. RESULTS: Of 936,371 primary care charts analyzed by the algorithm, 3535 patients (.4%) were determined to be high-risk for BE. Of these 3535 patients, only 1077 (30%) were screened for BE in clinical practice with endoscopy. The algorithm identified 2458 (70%) additional high-risk patients. Of the patients screened in clinical practice, 105 (10%) were found to have BE (10% with neoplasia). CONCLUSIONS: Numerous screening opportunities for BE are missed in the primary care setting of a large health system. Collaboration between gastroenterology and primary care services is needed to improve the screening rate.

A Highly Energy-Efficient Hyperdimensional Computing Processor for Biosignal Classification.

Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computationally minimal algorithm is dominated by costly hypervector memory storage, which grows linearly with the number of sensors. To address this, the memory is replaced with a light-weight cellular automaton for on-the-fly hypervector generation. The use of this technique is explored in conjunction with vector folding for various real-time classification latencies in post-layout simulation on an emotion recognition dataset with 200 channels. The proposed architecture achieves 39.1 nJ/prediction; a 4.9× energy efficiency improvement, 9.5× per channel, over the state-of-the-art HDC processor. At maximum throughput, the architecture achieves a 10.7× improvement, 33.5× per channel. An optimized support vector machine (SVM) processor is designed in this work for the same use-case. HDC is 9.5× more energy-efficient than the SVM, paving the way for it to become the paradigm of choice for high-accuracy, on-board biosignal classification.

Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata.

In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human-computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of > 76% for valence and > 73% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.

Endo-hepatology: An emerging field.

Gastroenterologists have long been spearheading the care of patients with various forms of liver disease. The diagnosis and management of liver disease has traditionally been a combination of clinical, laboratory, and imaging findings coupled with percutaneous and intravascular procedures with endoscopy largely limited to screening for and therapy of esophageal and gastric varices. As the applications of diagnostic and therapeutic endoscopic ultrasound (EUS) have evolved, it has found a particular niche within hepatology now coined endo-hepatology. Here we discuss several EUS-guided procedures such as liver biopsy, shear wave elastography, direct portal pressure measurement, paracentesis, as well as EUS-guided therapies for variceal hemorrhage.