A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.
Link: https://doi.org/10.1001/jamacardio.2024.0595 Authors: Oikonomou, Evangelos K; Holste, Gregory; Yuan, Neal; Coppi, Andreas; McNamara, Robert L; Haynes, Norrisa A; Vora, Amit N; Velazquez, Eric J; Li, Fan; Menon, Venu; Kapadia, Samir R; Gill, Thomas M; Nadkarni, Girish N; Krumholz, Harlan M; Wang, Zhangyang; Ouyang, David; Khera, Rohan Abstract: Aortic stenosis (AS) is a major public health…
Utilizing large language models in breast cancer management: systematic review.
Link: https://doi.org/10.1007/s00432-024-05678-6 Authors: Sorin, Vera; Glicksberg, Benjamin S; Artsi, Yaara; Barash, Yiftach; Konen, Eli; Nadkarni, Girish N; Klang, Eyal Abstract: Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in…
Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.
Link: https://doi.org/10.1101/2024.03.18.24304401 Authors: Anandakrishnan, Nanditha; Yi, Zhengzi; Sun, Zeguo; Liu, Tong; Haydak, Jonathan; Eddy, Sean; Jayaraman, Pushkala; DeFronzo, Stefanie; Saha, Aparna; Sun, Qian; Yang, Dai; Mendoza, Anthony; Mosoyan, Gohar; Wen, Huei Hsun; Schaub, Jennifer A; Fu, Jia; Kehrer, Thomas; Menon, Rajasree; Otto, Edgar A; Godfrey, Bradley; Suarez-Farinas, Mayte; Leffters, Sean; Twumasi, Akosua; Meliambro, Kristin; Charney,…
Corrigendum to “Post-operative urinary retention is impacted by neuromuscular block reversal agent choice: A retrospective cohort study in US hospital setting” [Journal of Clinical Anesthesia Volume 93 (2024) 111344].
Link: https://doi.org/S0952-8180(24)00052-7 Authors: Bash, Lori D; Turzhitsky, Vladimir; Mark, Robert J; Hofer, Ira S; Weingarten, Toby N Abstract:
Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound.
Link: https://doi.org/10.1101/2024.03.10.24304044 Authors: Oikonomou, Evangelos K; Holste, Gregory; Coppi, Andreas; McNamara, Robert L; Nadkarni, Girish N; Baloescu, Cristiana; Krumholz, Harlan M; Wang, Zhangyang; Khera, Rohan Abstract: Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. To develop…
Relationship Between Cognitive Impairment and Depression Among Middle Aged and Older Adults in Primary Care.
Link: https://doi.org/10.1177/23337214231214217 Authors: Federman, Alex D; Becker, Jacqueline; Carnavali, Fernando; Rivera Mindt, Monica; Cho, Dayeon; Pandey, Gaurav; Chan, Lili; Curtis, Laura; Wolf, Michael S; Wisnivesky, Juan P Abstract:
A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.
Link: https://doi.org/10.1101/2023.09.28.23296234 Authors: Oikonomou, Evangelos K; Holste, Gregory; Yuan, Neal; Coppi, Andreas; McNamara, Robert L; Haynes, Norrisa; Vora, Amit N; Velazquez, Eric J; Li, Fan; Menon, Venu; Kapadia, Samir R; Gill, Thomas M; Nadkarni, Girish N; Krumholz, Harlan M; Wang, Zhangyang; Ouyang, David; Khera, Rohan Abstract: Aortic stenosis (AS) is a major public health challenge…
Publication: Novel Intracranial Pressure Monitoring Using Non-Invasive Deep Learning Approach
A research study published as a preprint on February 27, 2024, by the AIMS Lab, showcases an innovative deep-learning approach that could redefine the field of intracranial pressure monitoring. In conditions like severe acute brain injuries (SABIs), including strokes and traumatic brain injuries, accurate measurement of intracranial pressure (ICP) can be significant for managing the…
Large language models: a primer and gastroenterology applications.
Link: https://doi.org/10.1177/17562848241227031 Authors: Shahab, Omer; El Kurdi, Bara; Shaukat, Aasma; Nadkarni, Girish; Soroush, Ali Abstract: Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions…
Publication: Machine Learning Boosts Precision in Social Determinants of Health Analysis
Area-level social determinants of health (SDOH) based on patients’ ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. However, using area-level SDOH measures as a substitute for individual SDOH measures may not be appropriate, especially in highly diverse urban neighbourhoods like New York City. On February 8, 2024, a…
Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care.
Link: https://doi.org/10.1101/2024.02.17.24302976 Authors: Khunte, Akshay; Sangha, Veer; Oikonomou, Evangelos K; Dhingra, Lovedeep S; Aminorroaya, Arya; Coppi, Andreas; Shankar, Sumukh Vasisht; Mortazavi, Bobak J; Bhatt, Deepak L; Krumholz, Harlan M; Nadkarni, Girish N; Vaid, Akhil; Khera, Rohan Abstract: Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows…
Publication: Simulation – What Is the Impact of Predictive AI in the Health Care Setting?
Models built on machine learning in health care can be victims of their own success, according to researchers at the Icahn School of Medicine and the University of Michigan. Their study assessed the impact of implementing predictive models on the subsequent performance of those and other models. Their findings—that using the models to adjust how care is delivered…
Publication: Deep Learning Insights on RV Function from ECG
In a groundbreaking study published on 29 December 2023 in the Journal of the American Heart Association, AIMS researchers have unveiled a novel approach to assess right ventricular (RV) size and function using deep learning-enabled electrocardiogram (ECG) analysis. Traditionally, assessing metrics like right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) required advanced imaging techniques,…
Associations between Sperm Epigenetic Age and Semen Parameters: An Evaluation of Clinical and Non-Clinical Cohorts.
Link: https://doi.org/10.3390/cimb46020101 Authors: Sawant, Savni; Oluwayiose, Oladele A; Nowak, Karolina; Maxwell, DruAnne L; Houle, Emily; Paskavitz, Amanda L; Saddiki, Hachem; Bertolla, Ricardo P; Pilsner, J Richard Abstract:
Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City.
Link: https://doi.org/10.1371/journal.pone.0297919 Authors: Takkavatakarn, Kullaya; Dai, Yang; Hsun Wen, Huei; Kauffman, Justin; Charney, Alexander; Coca, Steven G; Nadkarni, Girish N; Chan, Lili Abstract: Area-level social determinants of health (SDOH) based on patients’ ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could…
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.
Link: https://doi.org/10.1161/CIRCULATIONAHA.123.067750 Authors: Mayourian, Joshua; La Cava, William G; Vaid, Akhil; Nadkarni, Girish N; Ghelani, Sunil J; Mannix, Rebekah; Geva, Tal; Dionne, Audrey; Alexander, Mark E; Duong, Son Q; Triedman, John K Abstract: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains…
Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements.
Link: https://doi.org/10.1101/2024.01.30.24301974 Authors: Gulamali, Faris; Jayaraman, Pushkala; Sawant, Ashwin S; Desman, Jacob; Fox, Benjamin; Chang, Annie; Soong, Brian Y; Arivazaghan, Naveen; Reynolds, Alexandra S; Duong, Son Q; Vaid, Akhil; Kovatch, Patricia; Freeman, Robert; Hofer, Ira S; Sakhuja, Ankit; Dangayach, Neha S; Reich, David S; Charney, Alexander W; Nadkarni, Girish N Abstract: Increased intracranial pressure (ICP)…
Remote Monitoring and Artificial Intelligence: Outlook for 2050.
Link: https://doi.org/10.1213/ANE.0000000000006712 Authors: Feinstein, Max; Katz, Daniel; Demaria, Samuel; Hofer, Ira S Abstract: Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively.…
Quantitative Prediction of Right Ventricular Size and Function From the ECG.
Link: https://doi.org/10.1161/JAHA.123.031671 Authors: Duong, Son Q; Vaid, Akhil; My, Vy Thi Ha; Butler, Liam R; Lampert, Joshua; Pass, Robert H; Charney, Alexander W; Narula, Jagat; Khera, Rohan; Sakhuja, Ankit; Greenspan, Hayit; Gelb, Bruce D; Do, Ron; Nadkarni, Girish N Abstract: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional…
Evaluating the role of ChatGPT in gastroenterology: a comprehensive systematic review of applications, benefits, and limitations.
Link: https://doi.org/10.1177/17562848231218618 Authors: Klang, Eyal; Sourosh, Ali; Nadkarni, Girish N; Sharif, Kassem; Lahat, Adi Abstract: The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and…
A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.
Link: https://doi.org/S0933-3657(23)00264-6 Authors: Oh, Wonsuk; Jayaraman, Pushkala; Tandon, Pranai; Chaddha, Udit S; Kovatch, Patricia; Charney, Alexander W; Glicksberg, Benjamin S; Nadkarni, Girish N Abstract: Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However,…
Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.
Link: https://doi.org/10.1186/s12882-023-03424-7 Authors: Takkavatakarn, Kullaya; Oh, Wonsuk; Cheng, Ella; Nadkarni, Girish N; Chan, Lili Abstract: End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care…
Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.
Link: https://doi.org/10.3390/diagnostics13243613 Authors: Klang, Eyal; Sourosh, Ali; Nadkarni, Girish N; Sharif, Kassem; Lahat, Adi Abstract: Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review…
Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.
Link: https://doi.org/10.2196/51844 Authors: Abbott, Ethan E; Oh, Wonsuk; Dai, Yang; Feuer, Cole; Chan, Lili; Carr, Brendan G; Nadkarni, Girish N Abstract: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). We conducted a cluster…
Post-operative urinary retention is impacted by neuromuscular block reversal agent choice: A retrospective cohort study in US hospital setting.
Link: https://doi.org/S0952-8180(23)00294-5 Authors: Bash, Lori D; Turzhitsky, Vladimir; Mark, Robert J; Hofer, Ira S; Weingarten, Toby N Abstract: Perioperative neuromuscular blocking agents are pharmacologically reversed to minimize complications associated with residual neuromuscular block. Neuromuscular block reversal with anticholinesterases (e.g., neostigmine) require coadministration of an anticholinergic agent (e.g., glycopyrrolate) to mitigate muscarinic activity; however, sugammadex, devoid…
Effects of Mirikizumab on Histologic Resolution of Crohn’s Disease in a Randomized Controlled Phase 2 Trial.
Link: https://doi.org/10.1016/j.cgh.2023.11.010 Authors: Magro, Fernando; Protic, Marijana; De Hertogh, Gert; Chan, Lai Shan; Pollack, Paul; Jairath, Vipul; Carlier, Hilde; Hon, Emily; Feagan, Brian G; Harpaz, Noam; Pai, Rish; Reinisch, Walter Abstract: Histologic evaluation of mucosal healing in Crohn’s disease is an evolving treatment target. We evaluated histologic outcomes for mirikizumab efficacy and associations with endoscopic…
Development of the ehive Digital Health App: Protocol for a Centralized Research Platform.
Link: https://doi.org/10.2196/49204 Authors: Hirten, Robert P; Danieletto, Matteo; Landell, Kyle; Zweig, Micol; Golden, Eddye; Orlov, Georgy; Rodrigues, Jovita; Alleva, Eugenia; Ensari, Ipek; Bottinger, Erwin; Nadkarni, Girish N; Fuchs, Thomas J; Fayad, Zahi A Abstract: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital…
Genome-Wide Polygenic Risk Score for CKD in Individuals with APOL1 High-Risk Genotypes.
Link: https://doi.org/10.2215/CJN.0000000000000379 Authors: Vy, Ha My T; Coca, Steven G; Sawant, Ashwin; Sakhuja, Ankit; Gutierrez, Orlando M; Cooper, Richard; Loos, Ruth J F; Horowitz, Carol R; Do, Ron; Nadkarni, Girish N Abstract:
Reversal of neuromuscular block by sugammadex is associated with less postoperative respiratory dysfunction in the PACU compared with neostigmine: a retrospective study.
Link: https://doi.org/S0007-0912(23)00551-2 Authors: Lee, Andrew; Grogan, Tristan; Gabel, Eilon; Hofer, Ira S Abstract:
Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study.
Link: https://doi.org/10.1016/j.bjao.2023.100236 Authors: Epstein, Richard H; Perez, Olivia F; Hofer, Ira S; Renew, J Ross; Brull, Sorin J; Nemes, Réka Abstract: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action…
Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.
Link: https://doi.org/10.1101/2023.10.25.23297469 Authors: Jayaraman, Pushkala; Rajagopal, Madhumitha; Paranjpe, Ishan; Liharska, Lora; Suarez-Farinas, Mayte; Thompson, Ryan; Del Valle, Diane Marie; Beckmann, Noam; Oh, Wonsuk; Gulamali, Faris F; Kauffman, Justin; Gonzalez-Kozlova, Edgar; Dellepiane, Sergio; Vasquez-Rios, George; Vaid, Akhil; Jiang, Joy; Chen, Annie; Sakhuja, Ankit; Chen, Steven; Kenigsberg, Ephraim; He, John Cijiang; Coca, Steven G; Chan, Lili; Schadt,…
Per- and polyfluoroalkyl substances (PFAS) exposure and thyroid cancer risk.
Link: https://doi.org/10.1016/j.ebiom.2023.104831 Authors: van Gerwen, Maaike; Colicino, Elena; Guan, Haibin; Dolios, Georgia; Nadkarni, Girish N; Vermeulen, Roel C H; Wolff, Mary S; Arora, Manish; Genden, Eric M; Petrick, Lauren M Abstract: Although per- and polyfluoroalkyl substances (PFAS) exposure is a potential contributor to the increasing thyroid cancer trend, limited studies have investigated the association between…
Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD.
Link: https://doi.org/10.2215/CJN.0000000000000318 Authors: Lee, Arthur M; Xu, Yunwen; Hooper, Stephen R; Abraham, Alison G; Hu, Jian; Xiao, Rui; Matheson, Matthew B; Brunson, Celina; Rhee, Eugene P; Coresh, Josef; Vasan, Ramachandran S; Schrauben, Sarah; Kimmel, Paul L; Warady, Bradley A; Furth, Susan L; Hartung, Erum A; Denburg, Michelle R; , Abstract: Children with CKD are at…
Development and Validation of a Formative Assessment Tool for Nephrology Fellows’ Clinical Reasoning.
Link: https://doi.org/10.2215/CJN.0000000000000315 Authors: Boyle, Suzanne M; Martindale, James; Parsons, Andrew S; Sozio, Stephen M; Hilburg, Rachel; Bahrainwala, Jehan; Chan, Lili; Stern, Lauren D; Warburton, Karen M Abstract: Diagnostic errors are commonly driven by failures in clinical reasoning. Deficits in clinical reasoning are common among graduate medical learners, including nephrology fellows. We created and validated an…
A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease.
Link: https://doi.org/10.21203/rs.3.rs-3424565/v1 Authors: Eoli, Andrea; Ibing, Susanne; Schurmann, Claudia; Nadkarni, Girish N; Heyne, Henrike; Böttinger, Erwin Abstract: Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world’s population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322…
A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease.
Link: https://doi.org/10.1101/2023.10.12.23296926 Authors: Eoli, A; Ibing, S; Schurmann, C; Nadkarni, G N; Heyne, H O; Böttinger, E Abstract: Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world’s population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected…
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.
Link: https://doi.org/10.7326/M23-0949 Authors: Vaid, Akhil; Sawant, Ashwin; Suarez-Farinas, Mayte; Lee, Juhee; Kaul, Sanjeev; Kovatch, Patricia; Freeman, Robert; Jiang, Joy; Jayaraman, Pushkala; Fayad, Zahi; Argulian, Edgar; Lerakis, Stamatios; Charney, Alexander W; Wang, Fei; Levin, Matthew; Glicksberg, Benjamin; Narula, Jagat; Hofer, Ira; Singh, Karandeep; Nadkarni, Girish N Abstract: Substantial effort has been directed toward demonstrating uses of…
Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments.
Link: https://doi.org/10.1038/s41598-023-43436-9 Authors: Brin, Dana; Sorin, Vera; Vaid, Akhil; Soroush, Ali; Glicksberg, Benjamin S; Charney, Alexander W; Nadkarni, Girish; Klang, Eyal Abstract: The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed…
Clinical Informatics in Critical Care Medicine.
Link: https://doi.org/10.59249/WTTU3055 Authors: Nadkarni, Girish N; Sakhuja, Ankit Abstract: Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to…
Association of Racial Residential Segregation With Long-Term Outcomes and Readmissions After Out-of-Hospital Cardiac Arrest Among Medicare Beneficiaries.
Link: https://doi.org/10.1161/JAHA.123.030138 Authors: Abbott, Ethan E; Buckler, David G; Hsu, Jesse Y; Abella, Benjamin S; Richardson, Lynne D; Carr, Brendan G; Zebrowski, Alexis M Abstract: Background The national impact of racial residential segregation on out-of-hospital cardiac arrest outcomes after initial resuscitation remains poorly understood. We sought to characterize the association between measures of racial and…