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The ProFound Institute is a premiere digital education platform providing on-demand access to AI-centric content, tools and training. Through a diverse range of curated content, the institute provides insights on our cutting-edge, deep-learning solutions for artificial intelligence. Our ProFound Pioneers are comprised of world–renowned experts in clinical AI and radiology, delivering a portfolio of resources designed to assist in earlier detection of cancer. Join us as we empower physicians to deliver quality patient care through informative resources and knowledge based learning.
Podcast: The AI Solution That Saves Lives
Listen to the Healthcare Weekly podcast interview with iCAD Chairman & CEO, Mike Klein, to learn about the history of iCAD, ProFound AI, new features and functionalities iCAD plans to release later this year and the future of AI in healthcare.
Healthcare Weekly:
At the Forefront of Healthcare Innovation in Podcasts.
Podcast: The Role of 3D Mammography on Radiologist Burnout
Dr. Jeffrey Hoffmeister, MD, VP, Medical Director at iCAD, a global leader in medical technology providing innovative cancer detection and therapy solutions, discusses the role of 3D mammography on radiologist burnout and what steps practices can take to address it.
Dr. Jeffrey Hoffmeister is a family medicine doctor in Manhattan Beach, California. He received his medical degree from Washington University School of Medicine in St. Louis and has been in practice for more than 20 years.
Health Professional Radio
Video Spotlights
Discover the Power of ProFound AI™ for Digital Breast Tomosynthesis
Digital breast tomosynthesis (DBT) is rapidly replacing 2D mammography in breast cancer screening because of the clinical value it offers in cancer detection. However, it presents significant workload challenges for radiologists, which can affect patient care. ProFound AI™ is clinically proven to assist radiologists by improving cancer detection, decreasing reading times and reducing false positives and costly patient recalls.
Hear About the Power of ProFound AI from Leading Clinical Experts
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How is ProFound AI changing digital breast tomosynthesis?
In this video from RSNA 2019, Rodney Hawkins, vice president of product management for iCAD, discusses the advantages of using ProFound AI for Digital Breast Tomosynthesis, the first and only FDA-cleared software for DBT with AI.
ProFound AI™ for Digital Breast Tomosynthesis Technology
Rodney Hawkins, vice president of marketing, iCAD Inc. discusses how ProFound AI assists in the interpretation of breast tomosynthesis exams with exceptional reader study results.
Media Highlights
WMAZ: Navicent Health Adopts ProFound AI
13WMAZ features Jill Hancock, a Nurse Director from Navicent Health, the leading provider of healthcare in central and south Georgia, about their recent adoption of ProFound AI for DBT.
FOX54: Bridgeway Diagnostics Adopts ProFound AI
WFXG FOX54 showcases Bridgeway Diagnostics’ adoption of Profound AI, the first FDA-cleared 3D tomosynthesis software using artificial intelligence.
WKRC: TriHealth Talk Live with Anthony Antonoplos
TriHealth’s Anthony Antonoplos MD, a diagnostic radiologist, discussing the benefits he’s found while using ProFound AI, the first FDA-cleared digital breast tomosynthesis cancer detection software based on artificial intelligence (AI).
WTVM: Bridgeway Diagnostics Adopts ProFound AI
Bridgeway Diagnostics has recently adopted ProFound AI for DBT, reports WTVM9. Listen to Dr. Jason Hoover discuss the benefits of ProFound AI, the high-performing, concurrent-read, cancer detection and workflow solution for radiologists and patients.
AI for Mammography: Dr. Mark Traill
Courtesy of Fox 17 News at 10:00pm from Grand Rapids, Michigan. Dr. Mark Traill, radiologist at Metro Health, describes how ProFound AI can help detect breast cancer earlier.
ABC (Flint, MI): ProFound AI at McKenzie Health System
ABC in Flint, MI featured ProFound AI in a segment with Jay Smith, Director of Imaging at McKenzie Health System, breast cancer survivor Margaret Rhead, and her husband, Kim.
NBC/FOX (Flint, MI): ProFound AI is Changing the Game in the Fight against Breast Cancer
NBC/FOX in Flint, Michigan aired a segment on ProFound AI, featuring Jay Smith, Director of Imaging at McKenzie Health System, and breast cancer survivor Margaret Rhead.
WNEM 5: Dr. Hicks of RMI introducing ProFound AI
Randy Hicks, M.D., MBA, radiologist, co-owner and CEO at Regional Medical Imaging shares his excitement and discusses the value ProFound AI provides to his facility and patients.
ProFound Pioneers: Case Studies
PINK Breast Center Improves Accuracy and Enhances Patient Care with iCAD’s ProFound AI™ for Digital Breast TomosynthesisPINK Breast Center is a privately-owned imaging center specializing in breast care and ultrasound studies. Led by Lisa Sheppard, MD, PINK Breast Center has two locations in Flemington, NJ and Paterson, NJ; both locations are certified Breast Imaging Centers of Excellence. The practice can see up to 70 patients per day across both locations. For routine breast cancer screening, PINK Breast Center strives to offer rapid reads for patients, meaning results may be provided that same day, while the patient is still in the imaging center.Read MoreFrom Surviving to Thriving: How Regional Medical Imaging Enhanced Productivity and Profitability with ProFound AI™ for Digital Breast TomosynthesisAs the largest independent imaging group in Michigan with 10 locations across the state, Regional Medical Imaging (RMI) has been providing superior imaging services for 35 years. Coowner and CEO, Randy Hicks, MD, MBA, provides both a clinical and pragmatic approach to running the business, which has led RMI to become one of the leading radiology facilities in the state, with a team of experts in women’s imaging and other subspecialties.Read MoreWooster Community Hospital is a 175-bed, full service and acute-care facility serving residents of Wayne County, Ohio. The hospital offers a comprehensive range of inpatient and outpatient services, including radiology examinations. Of the 85,000 radiology exams performed each year, about 4,900 are mammography related. Gabriele Pedicelli, MD, a radiologist at Wooster Community Hospital, reviews the majority of breast screening cases at the facility, an average of 75-90 relative value units (RVUs) per day.Read MoreMarine Park Radiology Enhances Breast Cancer Screening and Radiologist Workflow with iCAD ProFound AI™ for Digital Breast TomosynthesisLocated in Brooklyn, NY, Marine Park Radiology, P.C. is a stand-alone radiology practice offering several modalities of imaging solutions, including mammography, CT, MRI, x-ray, DEXA and ultrasound. Led by Dr. Harold Tanenbaum and Dr. Richard Steinberg, Marine Park Radiology has been a fixture in the community for more than 30 years and is one of the few remaining privately held facilities in the New York City metropolitan area.Read MoreRadiologie am Theater is a private radiology practice that operates three offices in and around Paderborn, Germany. Since the 2006 debut of the German National Breast Screening Program, the center has offered screening services led by Dr. Axel Gräwingholt, head of the Department of Mammography Screening and clinical co-chair on the guideline group of the European Commission Initiative, on Breast Cancer (ECIBC), an initiative developing new evidence-based recommendations of guidelines for the whole breast cancer care pathway.Read MoreImaging for Women Offers Artificial Intelligence Solution for Digital Breast TomosynthesisImaging for Women opened its doors in 1997 as Kansas City’s first stand-alone women’s imaging center because Dr. Mark Malley envisioned a better way for women to experience healthcare.Its philosophy has always been to offer patients the best possible experience when visiting the facility. To accomplish this, Imaging for Women utilizes the most innovative technology on the market today including digital breast tomosynthesis (DBT) or 3D mammography and whole breast ultrasound that is performed by highly-skilled, certified technologists that make patient comfort and compassion a top priority.Read More
Clinical Case Studies & White Papers
The addition of DBT to full-field digital mammography (FFDM) improves radiologist performance by increasing cancer detection rates [1-4] and lowering recall rates [2-7], but also increases reading time almost two-fold [1, 8, 9], compared to 2D alone. Thus, ProFound AI was designed to maintain or improve radiologist clinical performance, while significantly reducing reading time.
Improving Reading Time of Digital Breast Tomosynthesis with Concurrent Computer Aided Detection
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Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches
Read More
Workfow improvements for digital breast tomosynthesis: computerized generation of enhanced |synthetic images
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iCAD Deep Learning AI for Breast Cancer Detection and Risk
Peer-Reviewed Journal Articles and Scientific Meeting Presentations
Deep Learning AI for Breast Cancer Detection Peer-Reviewed Journal Articles
- Graewingholt A, Rossi PG. Retrospective analysis of the effect on interval cancer rate of adding an artificial intelligence algorithm to the reading process for two-dimensional full-field digital mammography. J Med Screen. 2021 Jan 12:969141320988049. doi: 10.1177/0969141320988049. Epub ahead of print. PMID: 33435812.
https://pubmed.ncbi.nlm.nih.gov/33435812/ - Graewingholt A, Duffy S. Retrospective comparison between single reading plus an artificial intelligence algorithm and two-view digital tomosynthesis with double reading in breast screening. J Med Screen. 2021 Jan 5:969141320984198. doi: 10.1177/0969141320984198. Epub ahead of print. PMID: 33402033.
https://pubmed.ncbi.nlm.nih.gov/33402033/ - Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Boatsman JE, Hoffmeister JW. Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radiol Artif Intell. 2019 Jul 31;1(4):e180096. doi: 10.1148/ryai.2019180096. PMID: 32076660; PMCID: PMC6677281.
https://pubs.rsna.org/doi/full/10.1148/ryai.2019180096 - James JJ, Giannotti E, Chen Y. Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography. Clin Radiol. 2018 Oct;73(10):886-892. doi: 10.1016/j.crad.2018.05.028. Epub 2018 Jun 30. PMID: 29970247.
https://pubmed.ncbi.nlm.nih.gov/29970247/ - Benedikt RA, Boatsman JE, Swann CA, Kirkpatrick AD, Toledano AY. Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study. AJR Am J Roentgenol. 2018 Mar;210(3):685-694. doi: 10.2214/AJR.17.18185. Epub 2017 Oct 24. PMID: 29064756.
https://pubmed.ncbi.nlm.nih.gov/29064756/ - Balleyguier C, Arfi-Rouche J, Levy L, Toubiana PR, Cohen-Scali F, Toledano AY, Boyer B. Improving digital breast tomosynthesis reading time: A pilot multi-reader, multi-case study using concurrent Computer-Aided Detection (CAD). Eur J Radiol. 2017 Dec;97:83-89. doi: 10.1016/j.ejrad.2017.10.014. Epub 2017 Oct 24. PMID: 29153373.
https://pubmed.ncbi.nlm.nih.gov/29153373/
Deep Learning AI for Breast Cancer Detection Scientific Meeting Presentations
- ECR 2021: Conant EF, Toledano AY, Periaswamy S, Hoffmeister JW, Nishikawa RM. Use of an artificial intelligence software to identify low-likelihood of cancer exams collected across our commercial digital tomosynthesis screening systems. [RPS 605]
https://pubmed.ncbi.nlm.nih.gov/32076660/ - SIIM 2020: Periaswamy S, Conant EF, Toledano AY, Go J, Boatsman JE, Hoffmeister JW. Breast cancer detection with stand-alone artificial intelligence compared to radiologists in digital breast tomosynthesis. [Course ID: 4012]
https://pubmed.ncbi.nlm.nih.gov/32076660/ - ECR 2020: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Haldankar H, Go J, Boatsman J, Hoffmeister J. Breast cancer detection by mammographic view with artificial intelligence in digital breast tomosynthesis. [RPS 605b-6]
https://pubmed.ncbi.nlm.nih.gov/32076660/ - ECR 2020: Heywang-Köbrunner SH, Jänsch A, Mieskes C, Hertlein M, Hacker A. The value of 2D-AI-based CAD for second or third reading tested on 17,910 screening mammograms. [RPS 702-4]
https://pubmed.ncbi.nlm.nih.gov/32076660/ - RSNA 2019: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Pike J, Boatsman JE, Hoffmeister JW. Improved breast cancer detection and reading time with concurrent use of deep learning-based artificial intelligence for digital breast tomosynthesis when interpreted with digital mammography versus synthetic mammography. [SSA01-07]
https://pubs.rsna.org/doi/full/10.1148/ryai.2019180096 - RSNA 2019: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Pike J, Boatsman JE, Hoffmeister JW. How artificial intelligence may help improve accuracy and reading times in the interpretation of digital breast tomosynthesis screening studies. [BR204-ED-TUA9]
https://pubs.rsna.org/doi/full/10.1148/ryai.2019180096 - SIIM 2019: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Pike J, Boatsman JE, Hoffmeister JW. Improved breast cancer detection and reading time with digital breast tomosynthesis for breast subspecialists and general radiologists with concurrent use of artificial intelligence. [Machine Learning: Other]
https://cdn.ymaws.com/siim.org/resource/resmgr/siim2019/abstracts/ML_Other_Hoffmeister.pdf - SBI 2019: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Pike J, Boatsman JE, Hoffmeister JW. Concurrent use of deep learning based artificial intelligence improves detection of breast cancer and reading time with digital breast tomosynthesis in women with dense and non-dense breasts. [Abstract ID: 582552]
https://pubmed.ncbi.nlm.nih.gov/32076660/ - ECR 2019: Conant EF, Periaswamy S, Fotin S, Go J, Pike J, Boatsman J, Hoffmeister J. Impact of breast cancer characteristics on reader performance with concurrent use of artificial intelligence with digital breast tomosynthesis. [C-1648]
https://epos.myesr.org/poster/esr/ecr2019/C-1648 - ECR 2019: Conant EF, Toledano AY, Periaswamy S, Fotin S, Go J, Pike J, Boatsman J, Hoffmeister J. Case examples to demonstrate positive and negative impacts of a deep learning based concurrent artificial intelligence system for digital breast tomosynthesis. [C-2151]
https://epos.myesr.org/poster/esr/ecr2019/C-2151/findings%20and%20procedure%20details - RSNA 2018: Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Hoffmeister JW, Boatsman JE. Improved accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis screening. [RC215-14]
- ECR 2018: James J, Giannotti E, Chen Y. Evaluation of a CAD-enhanced 2D synthetic mammogram: comparison with a standard synthethic mammogram & FFDM. [B-1636]
https://repository.lboro.ac.uk/articles/journal_contribution/Evaluation_of_a_computer-aided_detection_CAD_-enhanced_2D_synthetic_mammogram_comparison_with_standard_synthetic_2D_mammograms_and_conventional_2D_digital_mammography/9403274 - ECR 2017: Benedikt RA, Toledano AY, Boatsman J, Arfi Rouche J, Boyer B, Hoffmeister J, Balleyguier C. Concurrent CAD with digital breast tomosynthesis improves reading time and maintains performance for dedicated breast radiologists and general radiologists. [C-1177]
https://pubmed.ncbi.nlm.nih.gov/29153373 - RSNA 2016: Benedikt RA, Swann CA, Kirkpatrick AD, Toledano A, Periaswamy S, Boatsman JE, Go J, Hoffmeister JW. Concurrent CAD for digital breast tomosynthesis. [SSE02-01]
- RSNA 2016: Balleyguier C, Arfi-Rouche J, Levy L, Toubiana PR, Cohen-Scali F, Toledano A, Periaswamy S, Go J, Hoffmeister JW, Boyer B. Pilot reader study of concurrent CAD for digital breast tomosynthesis. [BR231-SD-MOA6]
- EUSOBI 2016: Arfi-Rouche J, Balleyguier C, Levy L, Toubiana P, Cohen-Scali F, Boyer B, Benedikt R, Boatsman J, Kirkpatrick A, Swann C, Toledano A, Periaswamy S, Go J, Hoffmeister J. Determining efficacy of concurrent CAD for digital breast tomosynthesis. [Poster 19]
- JFR 2015: Arfi-Rouche J, Boyer B, Levy L, Toubiana P, Cohen-Scali F, Toledano A, Periaswamy S, Go J, Hoffmeister J, Balleyguier C. Evaluation of the contribution of a diagnostic support system (CAD) in tomosynthesis. [11:10 Oct 16]
Deep Learning AI for Breast Cancer Risk Peer-Reviewed Journal Articles
- Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, Förnvik D, Sartor H, Mavaddat N, Easton D, Hall P. Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening. Radiology. 2020 Nov;297(2):327-333. doi: 10.1148/radiol.2020201620. Epub 2020 Sep 8. PMID: 32897160.
https://pubs.rsna.org/doi/abs/10.1148/radiol.2020201620
Deep Learning AI for Breast Cancer Risk Scientific Meeting Presentations
- SABCS 2020: Eriksson M, Czene K, Hall P. Identification of women at high risk of breast cancer and in need of supplementary screening – A cohort study. [RPS 602b-1]
https://pubmed.ncbi.nlm.nih.gov/32897160/ - ECR 2020: Eriksson M, Czene K, Zackrisson A, Hall P. Identification of women at high risk of breast cancer and in need of supplementary screening. [PS8-01]
Resources
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