Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Jeremy Romek Glissen Brown, M.D.

Co-Author

This page shows the publications co-authored by Jeremy Glissen Brown and Tyler Berzin.
Connection Strength

8.876
  1. Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). Clin Gastroenterol Hepatol. 2021 Sep 14.
    View in: PubMed
    Score: 0.959
  2. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am. 2021 Oct; 31(4):743-758.
    View in: PubMed
    Score: 0.948
  3. Preloaded 22-gauge fine-needle system facilitates placement of a higher number of fiducials for image-guided radiation therapy compared with traditional backloaded 19-gauge approach. Gastrointest Endosc. 2021 11; 94(5):953-958.
    View in: PubMed
    Score: 0.939
  4. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc. 2022 Jan; 34(1):4-12.
    View in: PubMed
    Score: 0.932
  5. EndoBRAIN-EYE and the SUN database: important steps forward for computer-aided polyp detection. Gastrointest Endosc. 2021 04; 93(4):968-970.
    View in: PubMed
    Score: 0.929
  6. Deploying artificial intelligence to find the needle in the haystack: deep learning for video capsule endoscopy. Gastrointest Endosc. 2020 07; 92(1):152-153.
    View in: PubMed
    Score: 0.882
  7. Introducing computer-aided detection to the endoscopy suite. VideoGIE. 2020 Apr; 5(4):135-137.
    View in: PubMed
    Score: 0.859
  8. Artificial intelligence in gastrointestinal endoscopy: The future is almost here. World J Gastrointest Endosc. 2018 Oct 16; 10(10):239-249.
    View in: PubMed
    Score: 0.392
  9. Reply. Gastroenterology. 2021 05; 160(6):2212-2213.
    View in: PubMed
    Score: 0.229
  10. Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy. Endoscopy. 2021 09; 53(9):937-940.
    View in: PubMed
    Score: 0.226
  11. Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos. Endosc Int Open. 2020 Oct; 8(10):E1448-E1454.
    View in: PubMed
    Score: 0.225
  12. Using Computer-Aided Polyp Detection During Colonoscopy. Am J Gastroenterol. 2020 07; 115(7):963-966.
    View in: PubMed
    Score: 0.220
  13. Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality. J Clin Gastroenterol. 2020 07; 54(6):554-557.
    View in: PubMed
    Score: 0.220
  14. Regulatory considerations for artificial intelligence technologies in GI endoscopy. Gastrointest Endosc. 2020 10; 92(4):801-806.
    View in: PubMed
    Score: 0.219
  15. Using Computer-Aided Polyp Detection During Colonoscopy. Am J Gastroenterol. 2020 May 13.
    View in: PubMed
    Score: 0.218
  16. EUS-guided fiducial placement for pancreatobiliary malignancies: safety, infection risk, and use of peri-procedural antibiotics. Endosc Int Open. 2020 Feb; 8(2):E179-E185.
    View in: PubMed
    Score: 0.214
  17. The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study. Therap Adv Gastroenterol. 2020; 13:1756284820979165.
    View in: PubMed
    Score: 0.057
  18. Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study. Gastroenterology. 2020 10; 159(4):1252-1261.e5.
    View in: PubMed
    Score: 0.055
  19. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 04; 5(4):343-351.
    View in: PubMed
    Score: 0.053
  20. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 10; 68(10):1813-1819.
    View in: PubMed
    Score: 0.050
  21. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018 10; 2(10):741-748.
    View in: PubMed
    Score: 0.049
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.