Farahani, Nasibeh Zanjirani

Abstract Asthma is a chronic inflammatory condition marked by episodic airway narrowing, with exercise-induced bronchoconstriction (EIB) a common trigger. Up to 70% of athletes experience EIB-related symptoms, yet current management relies on reactive treatment rather than early physiological monitoring. This study presents a proof-of-concept multimodal wearable platform integrating impedance pneumography (IPG), electrocardiography (ECG), and inertial measurement unit (IMU) sensing to detect early physiological signatures of EIB. Candidate biomarkers include elevated heart rate, increased respiratory rate, and reduced thoracic impedance amplitude. Real-time embedded algorithms perform on-device feature extraction and threshold-based decision logic without cloud dependence, while IMU data provide posture context for signal interpretation. The system was evaluated through subsystem validation and a resistive-breathing protocol simulating bronchoconstriction. Results demonstrate reliable detection of characteristic cardiorespiratory changes, establishing the feasibility of real-time wearable detection of early EIB markers and supporting future large-cohort validation and form-factor refinement.

Abstract Femtech, a sector dedicated to the development of technologies addressing women's health issues, holds significant potential to revolutionize healthcare for women. This study provides an overview of current Femtech solutions tailored to meet the unique needs of women. It analyzes the present landscape, highlighting key areas where artificial intelligence (AI) and generative technologies have made substantial impacts. Furthermore, the study explores strategies to foster future growth and broader adoption of Femtech within the healthcare sector with a glance at Artificial Intelligence. The ultimate objective is to advance the development of more effective, personalized, accessible, and equitable healthcare solutions for women. The study will review and categorize 56 women’s health-related applications and devices currently available on the US market, identifying existing gaps and areas for potential improvement.

Introduction: Hypertrophic cardiomyopathy (HCM) is a disease characterized by genotypic and phenotypic heterogeneity. Correct classification of genetic variation is critical for clinical interpretation. Herein, variants of uncertain significance (VUS) pose a dilemma in clinical practice, limiting physicians’ decision making and causing anguish to patients and family members. Furthermore, with increasing knowledge and changing guidelines, continuous reassessment is crucial to maintain usefulness in practice. This study sought to assess contemporary variant classification in a large cohort of patients with HCM. Methods: Retrospective analysis of all patients with HCM seen at Mayo Clinic and all patients with a variant in one of 8 genes with strong disease association for HCM were included. Genetic test reports were obtained from electronic health records along with their original variant classification assigned by either a commercial or research laboratory. Reassessment of variant pathogenicity was performed using the 2015 ACMG guideline criteria. Population frequency was obtained using Gnomad. Co-segregation presence or absence was determined by family history and co-segregation studies. Combined Annotation Dependent Depletion (CADD) was used as computational evidence of genetic deleterious effects for single nucleotide variants. Results: Between 2003 to 2019, a total of 1,782 patients underwent laboratory or commercial HCM genetic testing. Of these, 787 (44%) had positive genetic test with a variant at least 1 of the 8 genes. The original pathogenic classification was obtained for 754 patients. Of those, 430 (57%) variants where within the gene MYBPC3, 244 (32%) within MYH7 and 80 (11%) within either ACTC1, MYL2, MYL3, TINNI3, TNNT2 or TPM1. Overall, 222 patients (29%) originally had one or more variants classified as a VUS. Upon reassessment, 120 (54%) were reclassified as either pathogenic (33%), likely pathogenic (58%), benign (5%) or likely benign (3%). Conclusion: Periodic reassessment for HCM genetic variants, especially VUS, is critical for adequate patient care and management. Cascade screening could decrease patients’ uncertainty regarding their prognosis and management.

Abstract Hypertrophic Cardiomyopathy (HCM) is an inheritable heart disease with the highest rate of sudden cardiac death (SCD) in young adults. Implantable Cardioverter Defibrillator (ICD) therapy is recommended for HCM patients at high risk for SCD. Reviewing the recent clinical literature revealed the potential to improve the selection of candidates for ICD implantation. The current study uses information extracted from echocardiography reports to evaluate HCM patients with ICD and aims to provide a comparative insight into patients who benefited the most from the device therapy, including shock and anti-tachycardia pacing (ATP). The proposed interpretable machine learning approach has used the XGboost algorithm. The model’s performance was considered satisfactory, as evidenced by an accuracy score of 81% and an area under the receiver operating characteristic curve (AUC) value of 69% and SHapley Additive exPlanations (SHAP) identified common properties of HCM patients in each category and provided high-level reasoning and foundation for a clinical decision support tool.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Abstract According to the 2020 report of the American Heart Association’s Heart & Stroke Statistics report, nearly 1,000 people are dying daily because of sudden out-of-hospital cardiac arrests and unfortunately, their survival rate is as low as 10%. Hypertrophic Cardiomyopathy (HCM), a relatively rare genetic heart disease is one of these diseases but finding the right patient for the implantation of ICD is still a research question. Implantation of cardioverter-defibrillator (ICD) can save the life of some of these patients. Due to the complexity of the identification of HCM patients, financial burdens, and the clinical risks involved in the ICD implantation procedure, HCM patients will go into a monitoring state before reaching the implantation trigger. Our study cohort shows about 82% of HCM deaths, did not have an ICD, which highlights the need to improve the pre-screening algorithms. In the current paper, we have proposed a new deep learning-based unsupervised clustering technique to facilitate the prioritization of patients to undergo ICD device implantation. This model uses over 900 echocardiographic measurements to find patients who benefit more from the ICD implantation procedure. Our model was trained and tested over 6 years of echo reports collected at Mayo Clinic. This model can be used as a decision support assistant for cardiologists in finding the right HCM patient when decision-making is hard.