Artificial Intelligence and Dysphagia: Novel Solutions to Old Problems (original) (raw)
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Dysphagia
Based on a large number of pre-existing documented electronic health records (EHR), we developed a machine learning (ML) algorithm for detection of dysphagia and aspiration pneumonia. The aim of our study was to prospectively apply this algorithm in two large patient cohorts. The tool was integrated in the hospital information system of a secondary care hospital in Austria. Based on existing data such as diagnoses, laboratory, and medication, dysphagia risk was predicted automatically, and patients were stratified into three risk groups. Patients’ risk groups and risk factors were visualized in a web application. Prospective predictions of 1270 admissions to geriatric or internal medicine departments were compared with the occurrence of dysphagia or aspiration pneumonia of routinely documented events. The discriminative performance for internal medicine patients (n = 885) was excellent with an AUROC of 0.841, a sensitivity of 74.2%, and a specificity of 84.1%. For the smaller geriat...
Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases
Journal of Clinical Gastroenterology, 2021
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential o...
Military medicine, 2016
This report describes the development and preliminary analysis of a database for traumatically injured military service members with dysphagia. A multidimensional database was developed to capture clinical variables related to swallowing. Data were derived from clinical records and instrumental swallow studies, and ranged from demographics, injury characteristics, swallowing biomechanics, medications, and standardized tools (e.g., Glasgow Coma Scale, Penetration-Aspiration Scale). Bayesian Belief Network modeling was used to analyze the data at intermediate points, guide data collection, and predict outcomes. Predictive models were validated with independent data via receiver operating characteristic curves. The first iteration of the model (n = 48) revealed variables that could be collapsed for the second model (n = 96). The ability to predict recovery from dysphagia improved from the second to third models (area under the curve = 0.68 to 0.86). The third model, based on 161 cases,...
Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
Diagnostics
Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett’s esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, ...
Diagnostics
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is sti...
A Review of Applications of Artificial Intelligence in Gastroenterology
Cureus, 2021
Artificial intelligence (AI) is the science that deals with creating 'intelligent machines'. AI has revolutionized medicine because of its application in several fields across medicine like radiology, neurology, ophthalmology, orthopedics and gastroenterology. In this review, we intend to summarize the basics of AI, the application of AI in various gastrointestinal pathologies till date as well as challenges/ problems related to the application of AI in medicine. Literature search using keywords like artificial intelligence, gastroenterology, applications, etc. were used. The literature search was done using Google Scholar, PubMed and ScienceDirect. All the relevant articles were gathered and relevant data were extracted from them. We concluded AI has achieved major feats in the past few decades. It has helped clinicians in diagnosing complex diseases, managing treatments as well as in predicting outcomes, all in all, which helps doctors from all over the globe in dispensing better healthcare services.