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Abstract BACKGROUND Hereditary hemorrhagic telangiectasia (HHT) is the second most common inherited bleeding disorder (IBD) worldwide, afflicting 1 in 5,000 people. HHT is likely the most morbid IBD of women. Severe, recurrent epistaxis, gastrointestinal (GI) bleeding, and solid organ arteriovenous malformations (AVMs) cause major clinical and psychosocial morbidity and reduced survival. In recognition of the serious morbidity of HHT as an IBD, in 2022 the U.S. Congress allocated funding to create CHORUS, the Comprehensive HHT Outcomes Registry of the United States (NCT06259292). Herein, we present the initial report from CHORUS. METHODS CHORUS is a prospective, multicenter, U.S. longitudinal registry enrolling unselected patients with HHT, collecting over 2400 data elements. Data were collected by trained research coordinators at enrollment and follow-up visits via the medical record and direct participant interviews. RESULTS Patients. 600 patients with genetically- or Curacao criteria-confirmed HHT were enrolled from November 2023 to April 2025 at 15 U.S. HHT Centers of Excellence. Median (range) age was 53 (0-88) years, and the cohort was 60% female. Despite most patients developing typical HHT disease manifestations by age 13, most HHT diagnoses (63%) were made during mid-to-late adulthood. Mucosal Bleeding. 568 patients (95%) developed recurrent spontaneous epistaxis, with 339 (60%) developing recurrent epistaxis prior to age 13; 43% had at least one nasal surgery/procedure and 22% had at least one medical therapy (e.g., antifibrinolytic or antiangiogenic) for epistaxis. 180 (30%) had a known diagnosis of chronic GI bleeding. Among post-menarche females, 124 (34%) had been diagnosed with heavy menstrual bleeding. 247 (41%) reported other clinically significant mucocutaneous oral or skin bleeding. 454 (76%) had developed moderate-to-severe HHT-associated mucocutaneous bleeding, defined as an Epistaxis Severity Score >4, bleeding requiring targeted pharmaceutical or surgical intervention, IV iron, and/or red cell transfusion. This includes 334 (56%) with moderate-to-severe HHT-associated epistaxis. CNS and Pulmonary Hemorrhage. 16 patients (3%) suffered one or more intracranial hemorrhages (ICH), with 10 (63%) suffering a first ICH by age 25. 13 (2%) suffered one or more pulmonary hemorrhages. Iron Deficiency and Anemia. 408 patients (68%) had been diagnosed with anemia (351, 59%) or iron deficiency (381, 64%). Of those, 247 (61%) and 150 (37%) required IV iron and RBC transfusion, respectively. Solid Organ AVMs. Brain, pulmonary, and liver AVMs were diagnosed in 81 (15%), 268 (45%), and 128 patients (21%), respectively. Just 30 with brain AVMs (37%) had these diagnosed by age 25. 3 patients (2%) had undergone liver transplantation. Thrombosis. Venous and arterial thromboembolism occurred in 44 (7%) and 38 patients (6%), respectively, including 34 (6%) with ischemic stroke (94% of whom had pulmonary AVMs). Other Serious HHT Complications. These included heart failure in 41 patients (7%), pulmonary hypertension in 44 (7%), chronic liver disease in 17 (2.8%), epilepsy in 24 (4%), severe bacterial/deep-seated tissue infection in 35 (6%), and brain abscess in 15 (3%). 205 (34%) developed migraine. Notably, serious CNS manifestations of HHT (brain AVM, ischemic stroke, intracranial hemorrhage, and/or epilepsy) occurred in 128 (21%). 241 (40%) required emergency room visits and/or hospital admissions specifically to manage HHT complications. CONCLUSIONS This first-of-its-kind HHT registry reliably describes for the first time the incidence and severity of numerous HHT complications. Most patients develop moderate-to-severe recurrent epistaxis and/or chronic GI bleeding as well as iron deficiency anemia (usually requiring IV iron and/or RBC transfusion). Heavy menstrual bleeding, thromboembolism, severe cardiopulmonary complications, and serious CNS complications are all substantially more common in HHT than previously thought. Notably, ICH occurred in 3% of patients, which approximates the ICH incidence of hemophilia in the pre-factor prophylaxis era and is ~2-fold and ~9-fold higher than in hemophilia A treated with modern factor prophylaxis (1.6%) and modern non-factor prophylactic agents (0.3%), respectively, per the ATHN registry. This 3% number may be an underestimation given the known high ICH mortality rate, pervasive delay in HHT diagnosis, and the fact that survival post-ICH is necessary to enroll in CHORUS.

Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. Each event was characterized using a 279-feature acoustic profile comprising Mel-frequency cepstral coefficients (MFCCs), their first-order derivatives (Δ-MFCCs), and six global spectral parameters. After normalization and dimensionality reduction with PCA and UMAP (cosine distance, 35 neighbors, minimum distance = 0.01), five clustering strategies were evaluated. K-Means (k = 5) achieved the most favorable balance between cluster quality (silhouette = 0.60; Calinski–Harabasz = 19,165; Davies–Bouldin = 0.68) and interpretability, consistently identifying five acoustic patterns: single-burst, multiple-burst, harmonic, random-continuous, and multi-modal. Temporal modeling of clustered events further revealed distinct sequential dynamics, with Single-Burst events showing the longest dwell times, random continuous the shortest, and strong diagonal elements in the transition matrix confirming measurable state persistence. Frequent transitions between random continuous and multi-modal states suggested dynamic exchanges between transient and overlapping motility patterns. Together, these findings demonstrate that unsupervised PEG-based analysis can capture both acoustic variability and temporal organization of bowel sounds. This annotation-free approach provides a scalable framework for real-time gastrointestinal monitoring and holds potential for clinical translation in conditions such as postoperative ileus, bowel obstruction, irritable bowel syndrome, and inflammatory bowel disease.

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.

Objectives: To determine the feasibility of reduced-dose chest computed tomographic angiography (CTA) with convolutional neural network (CNN) denoising for detecting pulmonary arteriovenous malformations (pAVMs) in children with hereditary hemorrhagic telangiectasia (cwHHT). Methods: Fifteen cwHHT underwent a chest CTA (ie, a controlled “study” dose). Noise was inserted to simulate a quarter dose (QD) exam. Images were reconstructed using iterative reconstruction (IR) and our self-trained CNN denoising model. For each case, 3 sets of images were created: study dose (SD)+IR, QD+IR, and QD+CNN. Two thoracic radiologists independently scored each set to assess quality, spatial resolution, artifacts, and the presence of pAVMs using 4-level ordinal scales. Quantitative assessments of image quality were performed using contrast-to-noise ratios (CNRs) with comparisons made between the experimental conditions. Results: Thirteen of the 15 patients recruited with hereditary hemorrhagic telangiectasia (mean age: 9.3±4.5 y) were positive for pAVM by transthoracic contrast echocardiography. The sensitivities using QD+CNN were 0.85 and 1.00 for readers 1 and 2, respectively. This was compared with 0.69 and 0.84 using QD+IR versus 0.85 and 0.92 for SD+IR. Inter-reader agreement for pAVM detection utilizing QD+CNN was moderate and resulted in kappa=0.59 ( P =0.012). The subjective assessments for QD+CNN were comparable to the SD technique. Regression analysis of reader scores revealed improved quality in QD+CNN versus QD+IR ( P =0.001). Similarly, the QD+CNN condition demonstrated the highest CNRs. Conclusions: Reduced-dose chest CTA with CNN denoising provides a level of sensitivity comparable to standard dose CTA and high CNRs for the detection of pAVMs in cwHHT.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.

ABSTRACT Hereditary hemorrhagic telangiectasia (HHT, Osler‐Weber‐Rendu disease) is the second most common inherited bleeding disorder worldwide, affecting approximately 1 in 5000 people. Development of disease‐modifying and efficacious hemostatic agents to treat HHT has finally begun after decades without such medical therapies. However, the lack of consensus on standardized severity definitions, outcome criteria, and terminology remains a major obstacle to clinical investigation and therapeutic development in HHT. Additionally, with the ongoing repurposing of antiangiogenic agents and emerging development of novel HHT‐specific therapies, comparative clinical trials are expected in the future. Therefore, to end the problematic heterogeneity hindering these efforts, the Global Research and Medical Advisory Board (GRMAB) of the Cure HHT Foundation, an international group of recognized HHT experts, convened a conference of expert HHT clinician‐investigators from within GRMAB as well as invited external experts in HHT and bleeding disorders generally, patients with HHT, and patient advocates to define standard terminology and definitions for outcomes and severity classification for bleeding and anemia in HHT. These criteria and definitions should be adopted by regulators, investigators, and the pharmaceutical industry in the development and performance of interventional clinical trials and cohort studies to allow improved comparability between clinical trials, facilitate communication between clinicians and investigators, improve therapeutic guideline development, and provide a standardized framework for regulatory agencies. Search Strategy and Selection Criteria Evidence for this report was systematically identified and evaluated utilizing two search strategies in Ovid MEDLINE, described in full detail in the Appendix, pp.4–13. The searches were conducted on January 7, 2025. The titles and abstracts of each record were reviewed, and the inclusion criteria were applied to all search results to identify full text articles to be retrieved for further review. All retrieved full texts were then reviewed to reach a final determination if a study met the inclusion criteria. Included references were then compiled into evidence tables, which were then utilized by the International Consensus Report Working Group throughout the development of report recommendations.

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution.

Abstract Introduction: Cystic fibrosis (CF) is a genetic disorder characterized by defective chloride ion transport, leading to thick mucus production, chronic respiratory infections, and other multisystem involvement. Although CF is often diagnosed in childhood, the condition can be missed or misdiagnosed in adults. Description of Case: We present the case of a 60-year-old male from India who was diagnosed with CF after a prolonged period of sinopulmonary symptoms. He initially presented in 2014. His medical history was pertinent for asthma, and multiple episodes of clinically and radiographically confirmed pneumonia and polypoid sinusitis requiring IV antibiotics and sinus surgery. He carried a prior diagnosis of allergic bronchopulmonary aspergillosis treated with itraconazole and steroids, as well as elevated IgE levels. His CT chest was notable for extensive bronchiectasis, bronchial wall thickening, and mucus plugging of both lungs. He had no known family history of CF. Sweat chloride testing was in the intermediate range (44, 47 mmol/L; normal <30 mmol/L, intermediate 30 to 59 mmol/L, abnormal >59 mmol/L). Given high suspicion for CF, cystic fibrosis transmembrane conductance regulator (CFTR) full gene analysis was pursued. Sequence analysis and multiplex ligation-dependent probe amplification of the CFTR gene detected heterozygous sequence changes c.1717-2A>T, classified as likely deleterious, and c.1367T>C (p.V456A), variant of uncertain significance. He was thus initially diagnosed as a CF carrier. He re-presented in 2024 with decline in pulmonary function testing and continued sinopulmonary infections. Genetic testing results from 2014 were re-analyzed. Between initial presentation and present, his V456A variant was upgraded to pathogenic. He met diagnostic criteria for CF with two pathogenic CFTR variants, a repeated intermediate sweat chloride (47 mmol/L) suggestive of CFTR protein dysfunction, and disease that could be attributed to CFTR dysfunction. He subsequently started TRIKAFTA® (elexacaftor/tezacaftor/ivacaftor and ivacaftor) and airway clearance therapy. Discussion: The diagnosis of CF in adults can be challenging, particularly in individuals without a known family history, intermediate sweat chloride testing, and with inconclusive genetic testing. Our understanding of CF in Asian populations is limited. CF is suspected to be underreported in patients with Southeast Asian ancestry in part due to genetic heterogeneity. The interpretation of certain genetic variants can evolve over time. A variant of unknown significance may later be reclassified as pathogenic, revealing its true role in disease development. This highlights the critical importance of regularly re-evaluating genetic test results.