Detection of elusive polyps using a large-scale artificial intelligence system (with videos).

BACKGROUND AND AIMS: Colorectal cancer is a leading cause of death. Colonoscopy is the criterion standard for detection and removal of precancerous lesions and has been shown to reduce mortality. The polyp miss rate during colonoscopies is 22% to 28%. DEEP DEtection of Elusive Polyps (DEEP2) is a new polyp detection system based on deep learning that alerts the operator in real time to the presence and location of polyps. The primary outcome was the performance of DEEP2 on the detection of elusive polyps. METHODS: The DEEP2 system was trained on 3611 hours of colonoscopy videos derived from 2 sources and was validated on a set comprising 1393 hours from a third unrelated source. Ground truth labeling was provided by offline gastroenterologist annotators who were able to watch the video in slow motion and pause and rewind as required. To assess applicability, stability, and user experience and to obtain some preliminary data on performance in a real-life scenario, a preliminary prospective clinical validation study was performed comprising 100 procedures. RESULTS: DEEP2 achieved a sensitivity of 97.1% at 4.6 false alarms per video for all polyps and of 88.5% and 84.9% for polyps in the field of view for less than 5 and 2 seconds, respectively. DEEP2 was able to detect polyps not seen by live real-time endoscopists or offline annotators in an average of .22 polyps per sequence. In the clinical validation study, the system detected an average of .89 additional polyps per procedure. No adverse events occurred. CONCLUSIONS: DEEP2 has a high sensitivity for polyp detection and was effective in increasing the detection of polyps both in colonoscopy videos and in real procedures with a low number of false alarms. (Clinical trial registration number: NCT04693078.).

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p = 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting.

Engineered Vascularized Muscle Flap.

One of the main factors limiting the thickness of a tissue construct and its consequential viability and applicability in vivo, is the control of oxygen supply to the cell microenvironment, as passive diffusion is limited to a very thin layer. Although various materials have been described to restore the integrity of full-thickness defects of the abdominal wall, no material has yet proved to be optimal, due to low graft vascularization, tissue rejection, infection, or inadequate mechanical properties. This protocol describes a means of engineering a fully vascularized flap, with a thickness relevant for muscle tissue reconstruction. Cell-embedded poly L-lactic acid/poly lactic-co-glycolic acid constructs are implanted around the mouse femoral artery and vein and maintained in vivo for a period of one or two weeks. The vascularized graft is then transferred as a flap towards a full thickness defect made in the abdomen. This technique replaces the need for autologous tissue sacrifications and may enable the use of in vitro engineered vascularized flaps in many surgical applications.

An engineered muscle flap for reconstruction of large soft tissue defects.

Large soft tissue defects involve significant tissue loss, requiring surgical reconstruction. Autologous flaps are occasionally scant, demand prolonged transfer surgery, and induce donor site morbidity. The present work set out to fabricate an engineered muscle flap bearing its own functional vascular pedicle for repair of a large soft tissue defect in mice. Full-thickness abdominal wall defect was reconstructed using this engineered vascular muscle flap. A 3D engineered tissue constructed of a porous, biodegradable polymer scaffold embedded with endothelial cells, fibroblasts, and/or myoblasts was cultured in vitro and then implanted around the femoral artery and veins before being transferred, as an axial flap, with its vascular pedicle to reconstruct a full-thickness abdominal wall defect in the same mouse. Within 1 wk of implantation, scaffolds showed extensive functional vascular density and perfusion and anastomosis with host vessels. At 1 wk posttransfer, the engineered muscle flaps were highly vascularized, were well-integrated within the surrounding tissue, and featured sufficient mechanical strength to support the abdominal viscera. Thus, the described engineered muscle flap, equipped with an autologous vascular pedicle, constitutes an effective tool for reconstruction of large defects, thereby circumventing the need for both harvesting autologous flaps and postoperative scarification.