Literature-Augmented Clinical Outcome Prediction

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. Based on each individual patient's clinical notes, we train language models (LMs) to find relevant papers and fuse them with information from notes to predict outcomes such as in-hospital mortality. We develop methods to retrieve literature based on noisy, information-dense patient notes, and to augment existing outcome prediction models with retrieved papers in a manner that maximizes predictive accuracy. Our approach boosts predictive performance on three important clinical tasks in comparison to strong recent LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25%.Comment: To appear in Findings of NAACL 2022. Code available at: https://github.com/allenai/BEE

Mask-conditioned latent diffusion for generating gastrointestinal polyp images

In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical field and the requirement of getting aid from experts for the time-consuming and costly medical data annotation process. In computer vision, image synthesis has made a significant contribution in recent years as a result of the progress of generative adversarial networks (GANs) and diffusion probabilistic models (DPM). Novel DPMs have outperformed GANs in text, image, and video generation tasks. Therefore, this study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given generated segmentation masks. Our experimental results show that our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. To test the usefulness of the generated data, we trained binary image segmentation models to study the effect of using synthetic data. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data. However, the results reflect that achieving good segmentation performance with synthetic data heavily depends on model architectures

SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation

Processing medical data to find abnormalities is a time-consuming and costly task, requiring tremendous efforts from medical experts. Therefore, Ai has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. AI tools highly depend on data for training the models. However, there are several constraints to access to large amounts of medical data to train machine learning algorithms in the medical domain, e.g., due to privacy concerns and the costly, time-consuming medical data annotation process. To address this, in this paper we present a novel synthetic data generation pipeline called SinGAN-Seg to produce synthetic medical data with the corresponding annotated ground truth masks. We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process. As a proof of concept, we used an open polyp segmentation dataset. By training UNet++ using both the real polyp segmentation dataset and the corresponding synthetic dataset generated from the SinGAN-Seg pipeline, we show that the synthetic data can achieve a very close performance to the real data when the real segmentation datasets are large enough. In addition, we show that synthetic data generated from the SinGAN-Seg pipeline improving the performance of segmentation algorithms when the training dataset is very small. Since our SinGAN-Seg pipeline is applicable for any medical dataset, this pipeline can be used with any other segmentation datasets

Patterns of antiplatelet agent use in the US

Background: The American Society of Gastrointestinal Endoscopy (ASGE) published updated guidelines in 2009 to help endoscopists manage the treatment of their patients who have been prescribed antiplatelet therapy (APT). Study aim: To assess the use of APT among endoscopists, and to identify factors guiding their use of APT while treating their patients. Method: A survey questionnaire was distributed to endoscopists at two national meetings to assess their usage of APT while treating patients during the peri-endoscopic period. Results: The survey was provided to 400 attendees of whom 239 (60 %) responded. Only 30 % of respondents followed the ASGE guidelines for treating their patients and 26 % percent of respondents withheld all APT before engaging in any patient procedure. Endoscopists’ decisions appeared to be influenced by their own particular experiences rather than any specific APT usage guidelines (46 % vs 22 %; P < 0.05). As expected, more endoscopists (P < 0.05) continued APT for patients who underwent low risk procedures (90 %) than for patients who underwent high risk procedures (47 %). Approximately 50 % of the respondents did not perform high risk procedures for patients prescribed aspirin therapy. Conclusions: About one-fourth of endoscopists surveyed discontinued APT treatment of patients who underwent any endoscopic procedure, and one-half of them discontinued use of non-steroidal anti-inflammatory drug treatment of patients who underwent a high risk endoscopic procedure. Inappropriate withdrawal of APT medications may expose patients to unnecessary risks, and efforts to improve endoscopists’ application of ASGE guidelines for the use of APT to treat patients during the peri-endoscopic period are warranted

Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement

The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing with supporting clinical evidence. Nevertheless, their implementation is not going smoothly for a variety of reasons, including lack of data on clinical benefits and cost-effectiveness, lack of trustworthy guidelines, uncertain indications, and cost for implementation. To address this issue and better guide practitioners, the World Endoscopy Organization (WEO) has provided its perspective about the status of AI in colonoscopy as the position statement. WEO Position Statement: Statement 1.1: Computer-aided detection (CADe) for colorectal polyps is likely to improve colonoscopy effectiveness by reducing adenoma miss rates and thus increase adenoma detection; Statement 1.2: In the short term, use of CADe is likely to increase health-care costs by detecting more adenomas; Statement 1.3: In the long term, the increased cost by CADe could be balanced by savings in costs related to cancer treatment (surgery, chemotherapy, palliative care) due to CADe-related cancer prevention; Statement 1.4: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADe to support its use in clinical practice; Statement 2.1: Computer-aided diagnosis (CADx) for diminutive polyps (≤5 mm), when it has sufficient accuracy, is expected to reduce health-care costs by reducing polypectomies, pathological examinations, or both; Statement 2.2: Health-care delivery systems and authorities should evaluate the cost-effectiveness of CADx to support its use in clinical practice; Statement 3: We recommend that a broad range of high-quality cost-effectiveness research should be undertaken to understand whether AI implementation benefits populations and societies in different health-care systems

Complications of gastro-oesophageal reflux disease.

Gastro-oesophageal reflux disease (GORD) is on the rise with more than 20% of the western population reporting symptoms and is the most common gastrointestinal disorder in the United States. This increase in GORD is not exactly clear but has been attributed to the increasing prevalence of obesity, changing diet, and perhaps the decreasing prevalence of H. pylori infection. Complications of GORD could be either benign or malignant. Benign complications include erosive oesophagitis, bleeding and peptic strictures. Premalignant and malignant lesions include Barrett\u27s metaplasia, and oesophageal cancer. Management of both the benign and malignant complications can be challenging. With the use of proton-pump inhibitors, peptic strictures (i.e., strictures related to reflux) have significantly declined. Several aspects of Barrett\u27s management remain controversial including the stage in the disease process which needs to be intervened, type of the intervention and surveillance of these lesions to prevent development of high grade dysplasia and oesophageal adenocarcinoma