Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DLbased systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists. The diversity of POCUS applications and ultrasound equipment, each requiring specialized AI models and domain expertise, limits the use of DL as a generic solution. In this article, we highlight the most advanced potential applications of AI in POCUS tailored to high-yield models in automated image interpretations, with the premise of improving the accuracy and efficacy of POCUS scans.
Non-invasive continuous alcohol monitoring has potential applications in both population research and in clinical management of acute alcohol intoxication or chronic alcoholism. Current wearable monitors based on transdermal alcohol content (TAC) sensing are relatively bulky and have limited quantification accuracy. Here we describe the development of a discreet wearable transdermal alcohol (TAC) sensor in the form of a wristband or armband. This novel sensor can detect vapor-phase alcohol in perspiration from 0.09 ppm (equivalent to 0.09 mg/dL sweat alcohol concentration at 25 °C under Henry's Law equilibrium) to over 500 ppm at oneminute time resolution. The TAC sensor is powered by a 110 mAh lithium battery that lasts for over 7 days. In addition, the sensor can function as a medical "internet-of-things" (IoT) device by connecting to an Android smartphone gateway via Bluetooth Low Energy (BLE) and upload data to a cloud informatics system. Such wearable IoT sensors may enable largescale alcohol-related research and personalized management. We also present evidence suggesting a hypothesis that perspiration rate is the dominant factor leading to TAC measurement variabilities, which may inform more reproducible and accurate TAC sensor designs in the future.
Objectives: Competency assessment is a key component of point-of-care ultrasound (POCUS) training. The purpose of this study was to design a smartphone-based standardized direct observation tool (SDOT) and to compare a faculty-observed competency assessment at the bedside with a blinded reference standard assessment in the quality assurance (QA) review of ultrasound images. Methods: In this prospective, observational study, an SDOT was created using SurveyMonkey containing specific scoring and evaluation items based on the Council of Emergency Medicine Residency-Academy of Emergency Ultrasound: Consensus Document for the Emergency Ultrasound Milestone Project. Ultrasound faculty used the mobile phone-based data collection tool as an SDOT at the bedside when students, residents, and fellows were performing one of eight core POCUS examinations. Data recorded included demographic data, examination-specific data, and overall quality measures (on a scale of 1-5, with 3 and above being defined as adequate for clinical decision making), as well as interpretation and clinical knowledge. The POCUS examination itself was recorded and uploaded to QPath, a HIPAA-compliant ultrasound archive. Each examination was later reviewed by another faculty blinded to the result of the bedside evaluation. The agreement of examinations scored adequate (3 and above) in the two evaluation methods was the primary outcome.
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