About

David M. Crockett, MD, is an Assistant Professor in the Department of Emergency Medicine at the University of Utah. He is board certified in emergency medicine and practices at the University of Utah Emergency Department as well as the Common Spirit Holy Cross Emergency Department. He is currently serving as the Emergency Ultrasound Fellowship Director, where he is responsible for teaching emergency medicine residents, fellows, and medical students. His research interests include point-of-care ultrasound, image classification with machine learning, and clinical informatics. Dr. Crockett's educational background includes a B.S. in Biological Engineering from Cornell University, an M.D. from the Geisel School of Medicine at Dartmouth, and residency training in Emergency Medicine at the University of Tennessee Health Science Center College of Medicine, where he also served as Chief Resident. His fellowship in Ultrasound was completed at the University of Utah Health.

Research topics

• Artificial Intelligence
• Medicine
• Biology
• Cancer research
• Genetics
• Internal medicine
• Medical physics
• Pathology
• Oncology
• Radiology

Selected publications

• SonoGif.com: A Free Online Tool to Remove Protected Health Information From Any Ultrasound Clip

  Journal of Ultrasound in Medicine · 2025-03-26

  articleOpen access1st authorCorresponding

  BACKGROUND: The proliferation of online medical education, particularly in point-of-care ultrasound (POCUS), has been limited by challenges in removing protected health information (PHI) from imaging data. These challenges include PHI embedded in both metadata and within the ultrasound images themselves, complicating compliance with HIPAA standards. OBJECTIVE: To describe the development and functionality of SonoGif.com, a free, browser-based tool designed to facilitate the de-identification and sharing of ultrasound clips without the need for specialized software. METHODS: SonoGif was developed using JavaScript to run entirely within a web browser, preserving data privacy by ensuring ultrasound clips remain on the user's device during initial processing. DICOM files are parsed using the open-source dicomParser library, while standard video formats are rendered with native HTML5 Canvas APIs. Users can manually annotate images to obscure on-screen PHI. The resulting de-identified frames are transmitted to a secure server, where FFmpeg compiles them into shareable video formats. RESULTS: Since its public release in 2019, SonoGif has been used to de-identify over 3000 ultrasound clips by users worldwide, including those in low-resource settings. Its accessibility, simplicity, and adherence to privacy regulations have made it a valuable tool for medical educators and clinicians seeking to share ultrasound media for teaching and research. CONCLUSION: SonoGif is a free web-based application that allows for easy and secure removal of PHI from ultrasound media. It broadens global access to ultrasound education by eliminating technical barriers and enabling safe image sharing across diverse clinical and educational environments. The platform is available at https://sonogif.com with source code accessible on GitHub.

  PublisherOA PDFDOI

• RWD39 Validating the Utility of Synthetic Data Generation for Clinical Research

  Value in Health · 2024-06-01

  articleSenior author
  PublisherDOI

• Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

  Molecular Psychiatry · 2023 · 36 citations

  • Biology
  • Genetics

  Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

  PublisherOA PDFDOI

• Phase 2, multicenter, open-label basket trial of nab-sirolimus for patients with malignant solid tumors harboring pathogenic inactivating alterations in TSC1 or TSC2 genes (PRECISION I) (1300)

  Gynecologic Oncology · 2023-09-01 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• TP018/#1522 Phase 2, multicenter, open-label basket trial of nab-sirolimus for patients with malignant solid tumors harboring pathogenic inactivating alterations in TSC1 or TSC2 genes (precision I)

  2022 · 1 citations

  • Medicine
  • Cancer research
  • Internal medicine

  <h3>Objectives</h3> Albumin-bound (nab)-sirolimus, a novel mechanistic target of rapamycin inhibitor (mTORi) that utilizes nanoparticle technology to preferentially target tumors, is approved in the US for the treatment of adult patients with locally advanced unresectable or metastatic malignant perivascular epithelioid cell tumor (PEComa). In an exploratory analysis of the registrational trial of nab-sirolimus in advanced malignant PEComa (PMID: 34637337), 8/9 (89%) and 1/5 (20%) patients with TSC1 and TSC2 inactivating alterations, respectively, had confirmed response. <h3>Methods</h3> PRECISION I (NCT05103358) is a phase 2, open-label, multi-institutional basket trial evaluating efficacy and safety of nab-sirolimus in patients with alterations in TSC1 (Arm A) and TSC2 (Arm B) (figure 1). Patients ≥12 years old with malignant solid tumors harboring pathogenic inactivating alterations in TSC1 or TSC2 (confirmed by central review of next generation sequencing reports) who have progressed on standard therapies and are mTORi-naïve will be eligible. nab-Sirolimus will be administered IV at 100 mg/m² weekly on Days 1 and 8 of each 21-day cycle. The primary endpoint is overall response rate determined by independent review using RECIST v1.1; other endpoints are shown in figure 1. Enrollment is ongoing. The most frequent tumor types expected in this tissue-agnostic trial are lung, bladder, soft tissue sarcomas, uterine, colon, kidney, melanoma, liver, and esophageal, based on prevalence of TSC1 or TSC2 alterations (table 1). <h3>Results</h3> Trial in progress: there are no available results at the time of submission. <h3>Conclusions</h3> Trial in progress: there are no available results at the time of submission.

  PublisherOA PDFDOI

• A Stress Test of Artificial Intelligence: Can Deep Learning Models Trained From Formal Echocardiography Accurately Interpret Point‐of‐Care Ultrasound?

  Journal of Ultrasound in Medicine · 2022 · 10 citations

  1st authorCorresponding
  • Artificial Intelligence
  • Medicine
  • Artificial Intelligence

  OBJECTIVES: To test if a deep learning (DL) model trained on echocardiography images could accurately segment the left ventricle (LV) and predict ejection fraction on apical 4-chamber images acquired by point-of-care ultrasound (POCUS). METHODS: We created a dataset of 333 videos from cardiac POCUS exams acquired in the emergency department. For each video we derived two ground-truth labels. First, we segmented the LV from one image frame and second, we classified the EF as normal, reduced, or severely reduced. We then classified the media's quality as optimal, adequate, or inadequate. With this dataset we tested the accuracy of automated LV segmentation and EF classification by the best-in-class echocardiography trained DL model EchoNet-Dynamic. RESULTS: The mean Dice similarity coefficient for LV segmentation was 0.72 (N = 333; 95% CI 0.70-0.74). Cohen's kappa coefficient for agreement between predicted and ground-truth EF classification was 0.16 (N = 333). The area under the receiver-operating curve for the diagnosis of heart failure was 0.74 (N = 333). Model performance improved with video quality for the tasks of LV segmentation and diagnosis of heart failure, but was unchanged with EF classification. For all tasks the model was less accurate than the published benchmarks for EchoNet-Dynamic. CONCLUSIONS: Performance of a DL model trained on formal echocardiography worsened when challenged with images captured during resuscitations. DL models intended for assessing bedside ultrasound should be trained on datasets composed of POCUS images. Such datasets have yet to be made publicly available.

  PublisherDOI

• Bioinformatics Tools in Clinical Genomics

  2018-12-10

  book-chapter1st authorCorresponding
  PublisherDOI

• Cough and Dyspnea in a Sarcoma Patient

  Oxford University Press eBooks · 2016-02-01

  book1st authorCorresponding

  These case studies illustrate infections encountered in hospitals among patients with compromised immune systems. As a result of immunocompromise, the patients are vulnerable to common and uncommon infections. These cases are carefully chosen to reflect the most frequently encountered infections in the patient population, with an emphasis on illustrations and lucid presentations to explain state-of-the-art approaches in diagnosis and treatment. Common and uncommon presentations of infections are presented while the rare ones are not emphasized. The cases are written and edited by clinicians and experts in the field. Each of these cases highlights the immune dysfunction that uniquely predisposed the patient to the specific infection, and the cases deal with infections in the cancer patient, infections in the solid organ transplant recipient, infections in the stem cell recipient, infections in patients receiving immunosuppressive drugs, and infections in patients with immunocompromise that is caused by miscellaneous conditions.

  PublisherDOI

• 4 Essential Lessons for Adopting Predictive Analytics in Healthcare

  2016-01-06 · 2 citations

  article1st authorCorresponding

  The willingness to intervene is the golden key to harnessing the power of historical and real-time data. Information plus context equals knowledge. But predictions made solely for the sake of making a prediction are a waste of time and money. Predictors are most useful when their knowledge can be transferred into action. The willingness to intervene is the golden key to harnessing the power of historical forecasting a trend and ultimately changing behavior, both the predictor and the the trend originally occurred. Case in point: mediating hospital readmissions. THE ECONOMY OF PREDICTION Academically speaking, predicting hospital readmissions is a very active topic. Thus far in 2013, 36 peer-reviewed journal articles have been published on the subject along with three additional review articles. Highlighting this rapidly growing patients,1 the relationship between readmission and mortality rates,2 and a systematic review of tools for predicting severe adverse events.3 Prediction 4, 5 or within pediatric populations are also very active.6, 7

  Publisher

• Bioinformatics Tools in Clinical Genomics

  2014-09-05 · 2 citations

  book-chapter1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Megan S. Lim

  Memorial Sloan Kettering Cancer Center

  98 shared

• Kojo S.J. Elenitoba‐Johnson

  Memorial Sloan Kettering Cancer Center

  97 shared

• Alan L. Rockwood

  University of Utah

  27 shared

• Rong Mao

  ARUP Laboratories (United States)

  21 shared

• Hernando Escobar

  Center for Veterinary Medicine

  20 shared

• Mark M. Kushnir

  20 shared

• Peter E. Jensen

  University of Utah

  20 shared

• Julio C. Delgado

  19 shared

Awards & honors

• American Board of Emergency Medicine (Advanced Emergency Med…
• American Board of Emergency Medicine (Emergency Medicine)
• National Board of Echocardiography