Bruce, Charles J.

Abstract Background Artificial intelligence enhanced electrocardiograms (AI-ECG) have matured over the past 10 years. More recently, the AI-ECG estimate of biological sex has shown potential as a novel tool for cardiovascular risk stratification among women. Cardiovascular disease is a leading cause of pregnancy-related mortality; however, there have been limited approaches for estimating the physiological demands of pregnancy and its impact of maternal cardiovascular outcomes. Purpose To estimate pregnancy-related changes on AI-ECG-based sex and age estimation in a prospective cohort of otherwise healthy, complication free pregnant women. Methods This secondary analysis of the SPEC-AI Nigeria Clinical Trial identified pregnant women without any pregnancy-related complications (at baseline or during follow-up) who had at least two 12 lead ECGs recorded during the study. The ECGs were analyzed with previously-developed algorithms to predict age and biological sex. To address bias in the AI-ECG age estimate, a reference cohort of 25,144 healthy patients was analyzed using quantile regression to estimate the 75th percentile of the reference distribution as a function of age. The estimate of biological sex was used to determine a sex discordance score with a discordance of 0.2 considered clinically relevant. To simplify the trends in the data, the ECGs were categorized as during pregnancy (<=28 days prior to birth), peripartum (+/- 28 days of birth), and postpartum (28+ days after birth), and mixed effects models with a random intercept were used for analysis. Results A total of 207 females with a median age of 31 years (IQR 28 to 36 years) and normal blood pressures (systolic: 110 (IQR 10 to 120); diastolic: 70 (IQR 60-77); mmHg) were included from 5 clinical sites in Nigeria. The majority (73%, 150/207) of participants were in their second trimester at time of enrollment and the median number of ECGs per participant was 3 (max 7). The sex discordance score was elevated in 38% of the women at baseline and this percentage fell to 23% postpartum; which resulted in a decrease in the mean discordance score of 0.03 (95% CI: -0.06 to 0.0; p=0.028). Conversely, the percentage of patients with elevated age prediction increased from 35% to 41% over the course of pregnancy. This resulted in an increase in the bias-corrected AI-ECG age of 3.0 years (95% CI: 1.5 to 4.6 years; p<0.001). Conclusion This study demonstrates the preliminary utility of serial AI-ECG based assessment as a marker for the biological effects of pregnancy. The increase in sex discordance score during pregnancy may be related to changes in cardiac structure, such as increased ventricular mass and chamber dilation which often reverses postpartum. However, the impact on age estimation appeared to persist in the early postpartum period. Further study is warranted to associate any changes observed with long term maternal or fetal outcomes.Figure

Abstract Valvular heart disease (VHD) is emerging as the next major epidemic in cardiovascular care, driven by the ageing population. The advent of less invasive alternatives to surgery—namely, transcatheter therapies—has revolutionized patient management, while simultaneously introducing new layers of complexity. Artificial intelligence (AI) holds significant promise to transform the care of patients with VHD across all dimensions, including diagnosis, prognostication, disease progression, and therapeutic decision-making. In this review, we provide a comprehensive overview of the current evidence, and discuss the challenges, risks, and future directions associated with the integration of AI in the management of VHD.

Background: Effective pain management is crucial for both comfort and outcomes, yet predicting and managing this pain is difficult. This study aimed to analyze postoperative pain in patients undergoing hand surgery at the Mayo Clinic Florida, examining how patient characteristics and anxiety affect pain outcomes. Methods: We conducted a single-arm clinical trial at Mayo Clinic Florida, recruiting patients undergoing hand surgery. Preoperative pain and anxiety were assessed using the Pain Catastrophizing Scale (PCS) and State-Trait Anxiety Inventory (STAI). Postoperatively, patients used an iPhone app to record pain levels and medication use every four hours. Devices were collected three days after surgery. We analyzed the relationship between demographics, PCS, STAI scores, and pain levels using linear and logistic regression models. All statistical tests were two-sided with significance set at p < 0.05, analyzed with R4.2.2. Results: Data were collected from 62 patients (62.9% women, 37.1% men) undergoing hand surgery. Participants were mainly White (90.3%), with 50% being in the middle-aged adult group. Most had low anxiety levels (80.6% STAI-S, 82.3% STAI-T) and low catastrophizing (61.3% PCS). Postoperative pain was low, with median scores between 1.0 and 2.0 over three days. Demographics, anxiety, and catastrophizing were not significant predictors of pain levels. Logistic regression showed time as a significant factor, with pain levels peaking on Day 3. Conclusions: Postoperative pain after hand surgery was generally low, with time being a significant predictor of increased pain. Demographic factors, anxiety, and catastrophizing did not significantly affect pain levels. Pain management should emphasize time-sensitive interventions and ongoing monitoring.

Abstract: Background: Allergic contact dermatitis is a common, pruritic, debilitating skin disease, affecting at least 20% of the population. Objective: To prospectively validate a computer vision algorithm across all Fitzpatrick skin types. Methods: Each participant was exposed to 10 allergens. The reference criterion was obtained 5 days after initial patch placement by a board-certified dermatologist. The algorithm processed photographs of the test site obtained on Day 5. Human performance in reading the photographs was also evaluated. Results: A total of 206 evaluable participants [mean age 39 years, 66% (136/206) female, and 47% with Fitzpatrick skin types IV–VI] completed testing. Forty-two percent (87/206) of participants experienced 1 or more allergic reaction resulting in a total of 132 allergic reactions. The model provided high discrimination (AUROC 0.86, 95% CI: 0.82–0.90) and specificity (93%, 95% CI: 92%–94%) but with lower sensitivity (58%, 95% CI: 49%–67%). Human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers. There were no serious adverse events. Conclusions: The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Unnecessary ED visits and transfers to hand clinics raise treatment costs and patient burden at trauma centers. In the present COVID-19 pandemic, needless transfers can increase patients’ risk of viral exposure. Therefore, this review analyzes different aspects of the remote diagnosis and triage of traumatic hand injuries. The most common file was photography, with the most common devices being cell phone cameras. Treatment, triage, diagnosis, cost, and time outcomes were assessed, showing concordance between teleconsultation and face-to-face patient evaluations. We conclude that photography and video consultations are feasible surrogates for ED visits in patients with traumatic hand injuries. These technologies should be leveraged to decrease treatment costs and potentially decrease the time to definitive treatment after initial evaluation.