Creating Computer Vision Models for Respiratory Status Detection.

This study aims to use computers to detect and recognize ventilation objects (masks and tubes) and their positions on the patient's face. We created two models: the You Only Look Once (YOLO) and the Transfer Learning (TL) models, to perform this computer vision task. The development processes and comparison of performance will be described in this paper. The TL model had a better performance (93%) compared to the YOLO model (93%). Clinical Relevance- Healthcare providers and researchers interested in the field of computer vision applied in medicine, specifically automatic object detection using video streams or real-time video streaming may benefit from findings reported.

Classification of Respiratory Conditions using Auscultation Sound.

Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.

Are We Ready for Video Recognition and Computer Vision in the Intensive Care Unit? A Survey.

OBJECTIVE: Video recording and video recognition (VR) with computer vision have become widely used in many aspects of modern life. Hospitals have employed VR technology for security purposes, however, despite the growing number of studies showing the feasibility of VR software for physiologic monitoring or detection of patient movement, its use in the intensive care unit (ICU) in real-time is sparse and the perception of this novel technology is unknown. The objective of this study is to understand the attitudes of providers, patients, and patient's families toward using VR in the ICU. DESIGN: A 10-question survey instrument was used and distributed into two groups of participants: clinicians (MDs, advance practice providers, registered nurses), patients and families (adult patients and patients' relatives). Questions were specifically worded and section for free text-comments created to elicit respondents' thoughts and attitudes on potential issues and barriers toward implementation of VR in the ICU. SETTING: The survey was conducted at Mayo Clinic in Minnesota and Florida. RESULTS: A total of 233 clinicians' and 50 patients' surveys were collected. Both cohorts favored VR under specific circumstances (e.g., invasive intervention and diagnostic manipulation). Acceptable reasons for VR usage according to clinicians were anticipated positive impact on patient safety (70%), and diagnostic suggestions and decision support (51%). A minority of providers was concerned that artificial intelligence (AI) would replace their job (14%) or erode professional skills (28%). The potential use of VR in lawsuits (81% clinicians) and privacy breaches (59% patients) were major areas of concern. Further identified barriers were lack of trust for AI, deterioration of the patient-clinician rapport. Patients agreed with VR unless it does not reduce nursing care or record sensitive scenarios. CONCLUSION: The survey provides valuable information on the acceptance of VR cameras in the critical care setting including an overview of real concerns and attitudes toward the use of VR technology in the ICU.

Deep Q-learning for Predicting Asthma Attack with Considering Personalized Environmental Triggers' Risk Scores.

The purpose of our present study was to develop a forecasting method that would help asthmatic individuals to take evasive action when the probability of an attack was at THEIR PERSONAL THRESHOLD levels. The results are encouraging. Risk factor analysis helps improve the agent's performance (by allowing it to consider personalized risk score of asthma attack triggers while making a decision and being able to ignore the non-triggers), increasing transparency of deep reinforcement learning in medicine applications (by using the results of analyzing risk factors and its association to take actions), and increase accuracy over time since the association risk factor indicators are also changing over time with more accuracy rate. It also brings the possibility of including population-based health in personalized health, which could support a more efficient self-management of chronic diseases.

Personalized Prediction of Asthma Severity and Asthma Attack for a Personalized Treatment Regimen.

Control of asthma is critical for disease management and quality of life. Asthma treatment depends on the patient demographic information (e.g., age), and disease severity, which is determined by: (1) how symptoms affect a patient's daily life, (2) measured lung function, and (3) estimated risk of having an asthma attack. In this paper, we will present the Tensorflow Text Classification (TC) method to classify a patient's asthma severity level. We will also propose a Qlearning method to train an agent through trials and errors to improve the prediction accuracy and create a personalized treatment regimen for asthma patients.