Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research.

OBJECTIVE: The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. METHODS: A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. RESULTS: After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. CONCLUSION: The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. SIGNIFICANCE: This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques.

BACKGROUND: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data. METHODS: This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores. RESULTS: The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value. CONCLUSIONS: This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Data-driven approach to quantify trust in medical devices using Bayesian networks.

Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.

Evaluation methodology for deep learning imputation models.

There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning-based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. In this article, we investigate the limitations of assessing deep learning-based imputation models by conducting a comparative analysis between RMSE and alternative metrics in the statistical literature including qualitative, predictive accuracy, statistical distance, and descriptive statistics. We design a new aggregated metric, called reconstruction loss (RL), to evaluate deep learning-based imputation models. We also develop and evaluate a novel imputation evaluation methodology based on RL. To minimize model and dataset biases, we use a regression imputation model and two different deep learning imputation models: denoising autoencoders and generative adversarial nets. We also use two tabular datasets from different industry sectors: health care and financial. Our results show that the proposed methodology is effective in evaluating multiple properties of the deep learning-based imputation model's reconstruction performance.

A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation.

The recent advances and availability of computer hardware, software tools, and massive digital data archives have enabled the rapid development of artificial intelligence (AI) applications. Concerns over whether AI tools can "communicate" decisions to radiologists and primary care physicians is of particular importance because automated clinical decisions can substantially impact patient outcome. A challenge facing the clinical implementation of AI stems from the potential lack of trust clinicians have in these predictive models. This review will expand on the existing literature on interpretability methods for deep learning and review the state-of-the-art methods for predictive uncertainty estimation for computer-assisted segmentation tasks. Last, we discuss how uncertainty can improve predictive performance and model interpretability and can act as a tool to help foster trust. Keywords: Segmentation, Quantification, Ethics, Bayesian Network (BN) © RSNA, 2021.

Medical Event Management for Future Deep Space Exploration Missions to Mars.

BACKGROUND: Long-duration exploration missions (LDEMs), such as voyages to Mars, will present unique medical challenges for astronaut crews, including communication delays and the inability to return to Earth early. Medical events threaten crewmember lives and increase the risk of mission failure. Managing a range of potential medical events will require excellent technical and nontechnical skills (NTSs). We sought to identify medical events with potential for rescue, range them according to the potential impact on crew health and mission success during LDEMs, and develop a list of NTSs to train for management of in-flight medical events. MATERIALS AND METHODS: Twenty-eight subject matter experts with specializations in surgery, medicine, trauma, spaceflight operations, NTS training, simulation, human factors, and organizational psychology completed online surveys followed by a 2-d in-person workshop. They identified and rated medical events for survivability, mission impact, and impact of crewmember NTSs on outcomes in space. RESULTS: Sudden cardiac arrest, smoke inhalation, toxic exposure, seizure, and penetrating eye injury emerged as events with the highest potential mission impact, greatest potential for survival, and that required excellent NTS for successful management. Key NTS identified to target in training included information exchange, supporting behavior, communication delivery, and team leadership/followership. CONCLUSIONS: With a planned Mars mission on the horizon, training countermeasures need to be developed in the next 3-5 y. These results may inform policy, selection, medical system design, and training scenarios for astronauts to manage in-flight medical events on LDEMs. Findings may extend to surgical and medical care in any rural and remote location.

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes.

There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions. However, as such evidence is limited to group-level analyzes, the extent to which they enable concussion detection at the individual-level is unclear. One promising avenue of research is the use of machine learning to create quantitative predictive models that can detect prior concussions in individuals. In this paper, we translate the recent group-level findings from ERP studies of concussed individuals into a machine learning framework for performing single-subject prediction of past concussion. We found that a combination of statistics of single-subject ERPs and wavelet features yielded a classification accuracy of 81% with a sensitivity of 82% and a specificity of 80%, improving on current practice. Notably, the model was able to detect concussion effects in individuals who sustained their last injury as much as 30 years earlier. However, failure to detect past concussions in a subset of individuals suggests that the clear effects found in group-level analyses may not provide us with a full picture of the neurophysiological effects of concussion.

An Environmental Scan of the National and Provincial Diagnostic Reference Levels in Canada for Common Adult Computed Tomography Scans.

Several regulatory bodies have agreed that low-dose radiation used in medical imaging is a weak carcinogen that follows a linear, non-threshold model of cancer risk. While avoiding radiation is the best course of action to mitigate risk, computed tomography (CT) scans are often critical for diagnosis. In addition to the as low as reasonably achievable principle, a more concrete method of dose reduction for common CT imaging exams is the use of a diagnostic reference level (DRL). This paper examines Canada's national DRL values from the recent CT survey and compares it to published provincial DRLs as well as the DRLs in the United Kingdom and the United States of America for the 3 most common CT exams: head, chest, and abdomen/pelvis. Canada compares well on the international scale, but it should consider using more electronic dose monitoring solutions to create a culture of dose optimization.

Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges.

Machine learning-based patient monitoring systems are generally deployed on remote servers for analyzing heterogeneous data. While recent advances in mobile technology provide new opportunities to deploy such systems directly on mobile devices, the development and deployment challenges are not being extensively studied by the research community. In this paper, we systematically investigate challenges associated with each stage of the development and deployment of a machine learning-based patient monitoring system on a mobile device. For each class of challenges, we provide a number of recommendations that can be used by the researchers, system designers, and developers working on mobile-based predictive and monitoring systems. The results of our investigation show that when developers are dealing with mobile platforms, they must evaluate the predictive systems based on its classification and computational performance. Accordingly, we propose a new machine learning training and deployment methodology specifically tailored for mobile platforms that incorporates metrics beyond traditional classifier performance.

Adaptive affective response identification for hearing threshold detection.

Emotional arousal, or affective patterns, can be probed using observable bioelectric signals, in particular using the fluctuations of electroencephalographic potentials from the human scalp. Hearing impairment related to increased threshold of audio tone detection may cause the loss of intelligibility of speech resulting in an innate automatic emotional response. An adaptive support vector machine can be trained to identify a subject's unique affective response based upon an audiogram hearing test. This paper presents the efficacy of our model, initial SVM classification data, and discusses potential application.

A social cybernetic analysis of simulation-based, remotely delivered medical skills training in an austere environment: developing a test bed for spaceflight medicine.

This paper describes analysis of medical skills training exercises that were conducted at an arctic research station. These were conducted as part of an ongoing effort to establish high fidelity medical simulation test bed capabilities in remote and extreme "space analogue" environments for the purpose studying medical care in spaceflight. The methodological orientation followed by the authors is that of "second order cybernetics," or the science of studying human systems where the observer is involved within the system in question. Analyses presented include the identification of three distinct phases of the training activity, and two distinct levels of work groups-- termed "first-order teams" and "second-order teams." Depending on the phase of activity, first-order and second-order teams are identified, each having it own unique structure, composition, communications, goals, and challenges. Several specific teams are highlighted as case examples. Limitations of this approach are discussed, as are potential benefits to ongoing and planned research activity in this area.

Systems modeling of space medical support architecture: topological mapping of high level characteristics and constraints.

The challenges associated with providing medical support to astronauts on long duration lunar or planetary missions are significant. Experience to date in space has included short duration missions to the lunar surface and both short and long duration stays on board spacecraft and space stations in low Earth orbit. Live actor, terrestrial analogue setting simulation provides a means of studying multiple aspects of the medical challenges of exploration class space missions, though few if any published models exist upon which to construct systems-simulation test beds. Current proposed and projected moon mission scenarios were analyzed from a systems perspective to construct such a model. A resulting topological mapping of high-level architecture for a reference lunar mission with presumed EVA excursion and international mission partners is presented. High-level descriptions of crew operational autonomy, medical support related to crew-member status, and communication characteristics within and between multiple teams are presented. It is hoped this modeling will help guide future efforts to simulate medical support operations for research purposes, such as in the use of live actor simulations in terrestrial analogue environments.