Advances for Treatment of Knee OC Defects.

Osteochondral (OC) defects are prevalent among young adults and are notorious for being unable to heal. Although they are traumatic in nature, they often develop silently. Detection of many OC defects is challenging, despite the criticality of early care. Current repair approaches face limitations and cannot provide regenerative or long-standing solution. Clinicians and researchers are working together in order to develop approaches that can regenerate the damaged tissues and protect the joint from developing osteoarthritis. The current concepts of tissue engineering and regenerative medicine, which have brought many promising applications to OC management, are overviewed herein. We will also review the types of stem cells that aim to provide sustainable cell sources overcoming the limitation of autologous chondrocyte-based applications. The various scaffolding materials that can be used as extracellular matrix mimetic and having functional properties similar to the OC unit are also discussed.

Management of knee osteoarthritis. Current status and future trends.

Osteoarthritis (OA) affects a large number of the population, and its incidence is showing a growing trend with the increasing life span. OA is the most prevalent joint condition worldwide, and currently, there is no functional cure for it. This review seeks to briefly overview the management of knee OA concerning standardized pharmaceutical and clinical approaches, as well as the new biotechnological horizons of OA treatment. The potential of biomaterials and state of the art of advanced therapeutic approaches, such as cell and gene therapy focused primarily on cartilage regeneration are the main subjects of this review. Biotechnol. Bioeng. 2017;114: 717-739. © 2016 Wiley Periodicals, Inc.

A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study.

BACKGROUND: Continuously growing medical knowledge and the increasing amount of data make it difficult for medical professionals to keep track of all new information and to place it in the context of existing information. A variety of digital technologies and artificial intelligence-based methods are currently available as persuasive tools to empower physicians in clinical decision-making and improve health care quality. A novel diagnostic decision support system (DDSS) prototype developed by Ada Health GmbH with a focus on traceability, transparency, and usability will be examined more closely in this study. OBJECTIVE: The aim of this study is to test the feasibility and functionality of a novel DDSS prototype, exploring its potential and performance in identifying the underlying cause of acute dyspnea in patients at the University Hospital Basel. METHODS: A prospective, observational feasibility study was conducted at the emergency department (ED) and internal medicine ward of the University Hospital Basel, Switzerland. A convenience sample of 20 adult patients admitted to the ED with dyspnea as the chief complaint and a high probability of inpatient admission was selected. A study physician followed the patients admitted to the ED throughout the hospitalization without interfering with the routine clinical work. Routinely collected health-related personal data from these patients were entered into the DDSS prototype. The DDSS prototype's resulting disease probability list was compared with the gold-standard main diagnosis provided by the treating physician. RESULTS: The DDSS presented information with high clarity and had a user-friendly, novel, and transparent interface. The DDSS prototype was not perfectly suited for the ED as case entry was time-consuming (1.5-2 hours per case). It provided accurate decision support in the clinical inpatient setting (average of cases in which the correct diagnosis was the first diagnosis listed: 6/20, 30%, SD 2.10%; average of cases in which the correct diagnosis was listed as one of the top 3: 11/20, 55%, SD 2.39%; average of cases in which the correct diagnosis was listed as one of the top 5: 14/20, 70%, SD 2.26%) in patients with dyspnea as the main presenting complaint. CONCLUSIONS: The study of the feasibility and functionality of the tool was successful, with some limitations. Used in the right place, the DDSS has the potential to support physicians in their decision-making process by showing new pathways and unintentionally ignored diagnoses. The DDSS prototype had some limitations regarding the process of data input, diagnostic accuracy, and completeness of the integrated medical knowledge. The results of this study provide a basis for the tool's further development. In addition, future studies should be conducted with the aim to overcome the current limitations of the tool and study design. TRIAL REGISTRATION: ClinicalTrials.gov NCT04827342; https://clinicaltrials.gov/ct2/show/NCT04827342.

Improving Emergency Department Patient-Physician Conversation Through an Artificial Intelligence Symptom-Taking Tool: Mixed Methods Pilot Observational Study.

BACKGROUND: Establishing rapport and empathy between patients and their health care provider is important but challenging in the context of a busy and crowded emergency department (ED). OBJECTIVE: We explore the hypotheses that rapport building, documentation, and time efficiency might be improved in the ED by providing patients a digital tool that uses Bayesian reasoning-based techniques to gather relevant symptoms and history for handover to clinicians. METHODS: A 2-phase pilot evaluation was carried out in the ED of a German tertiary referral and major trauma hospital that treats an average of 120 patients daily. Phase 1 observations guided iterative improvement of the digital tool, which was then further evaluated in phase 2. All patients who were willing and able to provide consent were invited to participate, excluding those with severe injury or illness requiring immediate treatment, with traumatic injury, incapable of completing a health assessment, and aged <18 years. Over an 18-day period with 1699 patients presenting to the ED, 815 (47.96%) were eligible based on triage level. With available recruitment staff, 135 were approached, of whom 81 (60%) were included in the study. In a mixed methods evaluation, patients entered information into the tool, accessed by clinicians through a dashboard. All users completed evaluation Likert-scale questionnaires rating the tool's performance. The feasibility of a larger trial was evaluated through rates of recruitment and questionnaire completion. RESULTS: Respondents strongly endorsed the tool for facilitating conversation (61/81, 75% of patients, 57/78, 73% of physician ratings, and 10/10, 100% of nurse ratings). Most nurses judged the tool as potentially time saving, whereas most physicians only agreed for a subset of medical specialties (eg, surgery). Patients reported high usability and understood the tool's questions. The tool was recommended by most patients (63/81, 78%), in 53% (41/77) of physician ratings, and in 76% (61/80) of nurse ratings. Questionnaire completion rates were 100% (81/81) by patients and 96% (78/81 enrolled patients) by physicians. CONCLUSIONS: This pilot confirmed that a larger study in the setting would be feasible. The tool has clear potential to improve patient-health care provider interaction and could also contribute to ED efficiency savings. Future research and development will extend the range of patients for whom the history-taking tool has clinical utility. TRIAL REGISTRATION: German Clinical Trials Register DRKS00024115; https://drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024115.

Supporting shared hypothesis testing in the biomedical domain.

BACKGROUND: Pathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses. RESULTS: In this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences. CONCLUSIONS: We evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.