Iyer, Vivek N

Photoplethysmography (PPG) is a non-invasive optical technique commonly used to measure heart rate and oxygen saturation, but its waveform contains additional physiological information that can be analyzed using artificial intelligence (AI). This narrative review summarizes the emerging applications of AI-based PPG in cardiovascular, respiratory, sleep, hemodynamic, pregnancy-related, and portal-hypertension assessment, with the aim of evaluating its potential beyond conventional monitoring and identifying barriers to clinical translation. The literature search was conducted using PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and SpringerLink. Additional relevant studies were identified through screening the reference lists of included articles. Studies published between 2002 and 2026 were identified to capture the development of PPG from conventional monitoring to newer AI-based applications. Human studies were prioritized, while relevant computational, simulated, synthetic, ex vivo, and technical studies were also included. Studies unrelated to PPG, duplicates, and studies with limited relevance were excluded. A total of 96 references were included, covering AI approaches such as convolutional and deep neural networks, ensemble methods, transfer learning, U-Net, generative adversarial networks, and Transformer-based models. Overall, the reviewed evidence suggests that AI-based PPG may support blood pressure estimation, atrial fibrillation detection, sleep and respiratory monitoring, vascular aging assessment, pulmonary hypertension screening, preeclampsia assessment, volume-status and compensatory-reserve assessment, and exploratory assessment related to portal hypertension. However, clinical translation remains limited by motion artifacts, sensor and measurement-site variability, skin-pigmentation-related bias, physiological and environmental influences, heterogeneous methods, limited external validation, and inconsistent clinical and regulatory standards.

Abstract Hereditary hemorrhagic telangiectasia (HHT), an autosomal dominant vasculopathy affecting 1 in 5000 individuals, is the second most common inherited bleeding disorder worldwide. Despite this prevalence, comprehensive data on disease manifestations and complications remain limited. To address this gap, the US Congress allocated funding leading to the Comprehensive HHT Outcomes Registry of the United States (CHORUS), a prospective, 15-center longitudinal registry enrolling unselected patients with confirmed HHT. In this initial report, we describe findings from the first 600 participants, with a median age of 53 (range, 0-88) years and 60% female. Despite most participants developing typical HHT manifestations by age 13 years, the majority (63%) were not diagnosed until mid-to-late adulthood. Recurrent spontaneous epistaxis occurred in 95% of participants, chronic gastrointestinal bleeding in 30%, and heavy menstrual bleeding in 35% of postmenarche females, together resulting in moderate-to-severe mucosal bleeding in 76%. Iron deficiency and/or anemia were diagnosed in 68%, with 41% requiring IV iron and 25% requiring red cell transfusions. Serious complications of solid-organ arteriovenous malformations were frequent, including intracranial hemorrhage (3%), pulmonary hemorrhage (2%), venous thromboembolism (7%), arterial thromboembolism (11%), heart failure (7%), and pulmonary hypertension (7%). These data from CHORUS, the first national US registry of its kind, provide reliable, real-world estimates of the incidence, prevalence, and severity of numerous HHT manifestations and complications. HHT has a high burden of moderate-to-severe bleeding, anemia, thrombosis, and major neurologic and cardiopulmonary complications. There is a mean interval between first symptoms and diagnosis of >2 decades, during which substantial, serious, and preventable HHT morbidity, including early intracranial hemorrhage, may occur. This trial was registered at clinicaltrials.gov as NCT06259292.

Abstract Hereditary hemorrhagic telangiectasia (HHT) results in recurrent severe epistaxis, chronic gastrointestinal bleeding, and other morbid manifestations. Pomalidomide was recently demonstrated to be efficacious for bleeding in HHT in the landmark PATH-HHT trial. Given that the vascular remodeling that results in bleeding improvement may require several months, characteristics associated with treatment response would be invaluable to plan treatment. Using data from PATH-HHT (NCT03910244), we identified characteristics associated with change from baseline in Epistaxis Severity Score (ESS; a well-validated 10-point HHT bleeding scale and the trial’s primary end point) using multivariable-adjusted repeated-measures models. Characteristics associated with a better response to pomalidomide at weeks 16 to 24 of the trial included a higher (more severe) baseline ESS (change from baseline, −0.76 [95% confidence interval (CI), −0.99 to −0.54] points per 1-point higher baseline score, P< .001) and concomitant antifibrinolytic therapy (−0.73 [95% CI, −1.48 to 0.01] vs no therapy, P = .05). Characteristics associated with a less robust response were older age (0.29 [95% CI, 0.05-0.53] per 10 years older, P = .02) and underlying ACVRL1 germ line mutation (0.62 [95% CI, −0.04 to 1.27] vs ENG, SMAD4, or mutation not known, P = .06]. Characteristics notably associated with placebo response (placebo effect) were patient-reported outcome measures, such that those experiencing more severe disease at baseline had less placebo effect. In conclusion, several baseline patient and disease characteristics were associated with pomalidomide response, findings that may help guide patient selection for the use of pomalidomide in HHT. This trial was registered at www.Clinicaltrials.gov as NCT03910244.

Hypertension has been traditionally known to be highlighted by mean blood pressure; however, emerging evidence exhibits that blood pressure variability (BPV), including short-term, day-to-day, and visit-to-visit fluctuations can have an implication across multiple body systems. Elevated BPV reflects repetitive hemodynamic stress, affecting the physiologic hemostasis contributing to vascular injury and end organ damage. This narrative review is a compilation of recent evidence on the prognostic value of BPV, explained by pathophysiology, various devices with its measurement approaches, and, essentially, the clinical implication of BPV and the use of such devices utilizing artificial intelligence. A comprehensive literature search across PubMed, Cochrane Library, Scopus, and Web of Science were conducted, focusing on observational studies, cohorts, randomized trials, and meta-analyses. Higher BPV has been associated with an increased risk of cardiovascular mortality, stroke, coronary events, and heart failure, the progression of chronic kidney disease, cognitive decline, and preeclampsia, among other end organ damage, despite mean blood pressure. The various pathophysiologic mechanisms include autonomic dysregulation, arterial stiffness, endothelial dysfunction, circadian rhythm alteration, and systemic inflammation, which result in vascular remodeling and multisystem damage. Antihypertensive medications such as calcium channel blockers and renin–angiotensin–aldosterone system inhibitors seem to reduce BPV; randomized trials have not specifically investigated their BPV-reducing effects. The aim of this review is to highlight that BPV is a dynamic marker of multisystem risk, and question how various AI-based devices can aid continuous BPV monitoring and patient specific risk stratification.

Abstract Hereditary hemorrhagic telangiectasia (HHT) causes severe recurrent epistaxis, chronic gastrointestinal bleeding, solid organ arteriovenous malformations, and significant anemia. Although it is the second most common inherited bleeding disorder worldwide, no approved therapies exist. The multicenter, US randomized, controlled PATH-HHT trial demonstrated efficacy of short-term pomalidomide treatment of epistaxis in HHT. However, questions regarding long-term safety, effectiveness, and utility for HHT-associated gastrointestinal bleeding remain. The PATH-HHT ATLAS (after trial longitudinal assessment study) was a multicenter US longitudinal observational study evaluating patients enrolled in PATH-HHT who continued pomalidomide after PATH-HHT through a poststudy drug access program, with ongoing close monitoring per PATH-HHT protocol and as mandated by the US Food and Drug Administration. Sixty-two patients treated with pomalidomide for HHT for up to 4.4 years were included. Significant, durable improvements in mean epistaxis severity score (5.55 points [baseline] to 2.80 points [month 12]; P< .0001) and mean hematologic support score (9.11 red cell unit equivalents [RUEs] during 6 months pretreatment to 5.73 RUEs [months 7-12]; P = .0056) were observed. Pomalidomide was less effective for gastrointestinal bleeding than for epistaxis, particularly in patients with high red blood cell transfusion requirements. Thirty-one patients (50%) underwent dose reduction from 4 mg daily, primarily due to neutropenia, but most maintained effectiveness at 2 or 3 mg daily. Over 105.2 patient-years of pomalidomide treatment, 0 patients developed peripheral neuropathy, 1 developed venous thromboembolism, and 5 developed serious infections. In conclusion, pomalidomide was effective for HHT-associated epistaxis over extended durations, albeit with nontrivial potential toxicities, and may be considered for certain patients with HHT. This trial was registered at www.clinicaltrials.gov as NCT07018401.

Background: Interstitial lung disease (ILD) involves numerous chronic pulmonary conditions that damage the lung parenchyma and alveolar interstitium. ILD has overlapping clinical and radiological features with other commonly seen cardiac and respiratory conditions. If not identified and treated in a timely manner, it may lead to irreversible fibrosis and a poor prognosis in the patient. The current diagnostic methods are either invasive or reliant on imaging or specialist interpretation, which can lead to diagnostic delay, increased radiation exposure, and healthcare costs. Lung crackles, often under-recognized as a non-specific feature of ILD, may serve as an important diagnostic clue in identifying not only the early stages of ILD but also its subtypes. This review explores the potential of analyzing the lung sounds in ILD through AI-based auscultation. Objective: To provide a comprehensive analysis of the pathophysiological stages of lung injury in ILD, the specific acoustic features, and the location associated with each ILD subtype and to evaluate the current state-of-the-art non-AI and AI methodologies that are used to diagnose ILD. This review aims to analyze the limitations associated with the current modalities and to envision AI-integrated auscultation as a powerful, cost-effective, non-invasive, radiation-free screening tool for early detection of ILD and its subtypes. Content Overview: The review begins with a detailed analysis of the lung sound pathophysiology, exploring the two-stage mechanism of alveolar epithelial injury and fibrosis formation. Existing hypotheses explaining the mechanism behind crackle production and the role of structural anatomy and surface tension in the generation of pathological lung sounds are examined. A tabulated summary of common ILD subtypes is provided, including their inciting events, pathogenesis, anatomical auscultation locations, and prognostic implications. Current diagnostic modalities for ILD, both non-AI and AI-based, are summarized along with their limitations, emphasizing the need for improved diagnostic tools. Discussion: Existing studies suggest that AI-based auscultation can match or exceed the current modalities in its sensitivity and specificity for detecting ILD-related crackles. Clinicians can identify the specific sound pattern and then correlate it with the ILD subtype and understand the prognosis in real time, thereby providing timely intervention to the patient. Additionally, AI-based auscultation can be used in resource-limited settings and can potentially reduce dependence on pulmonology expertise and radiation-based imaging for monitoring the condition. Conclusions: This literature review highlights the clinical potential of AI-based auscultation for early and accurate diagnoses of ILD. Understanding the associated pathological sounds, biomarkers, and genetic mutations linked to different subtypes opens avenues for future development of non-invasive diagnostic panels for ILD in clinical practice.

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.

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.