This is a list of selected links used as reference in the book: A Radiologist’s Overview of the AI Market – The first 100 Algorithms
Endnotes: Links and References by Chapter
Chapter 1 – Neuroradiology
Discussion on Viz.ai CMS reimbursement: https://lukeoakdenrayner.wordpress.com/2020/09/24/its-complicated-a-deep-dive-into-the-viz-medicare-ai-reimbursement-model/
Article delineating societal and healthcare costs associated with stroke: (https://jnis.bmj.com/content/early/2020/11/24/neurintsurg-2020-016897)
Article on ML volume measurements of lateral ventricles and cranial vault: https://www.sciencedirect.com/science/article/abs/pii/S1878875020327157?via%3Dihub
Chapter 2 – MSK
An algorithmic approach to reducing unexplained pain disparities in underserved populations
https://www.nature.com/articles/s41591-020-01192-7
Chapter 3 – Pediatric imaging
Medo hip leverages artificial intelligence to result in a screening process that is both economic, accurate, and reliable in a clinical setting.
Chapter 4 – Cardiac
Arterys founder talk with Stanford students
SCOT-HEART and PROMISE data: https://www.acc.org/latest-in-cardiology/articles/2018/05/21/06/37/coronary-cta-pro
CAD-RADs Article which discusses importance of plaque composition: https://cdn.ymaws.com/scct.org/resource/resmgr/cad-rads/scct_jcct_cad-rads.pdf
FFRCT paper: Douglas PS, De Bruyne B, Pontone G, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. J Am Coll Cardiol. 2016;68:435-445.
Narang A, Bae R, Hong H, et al. Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use. JAMA Cardiol. Published online February 18, 2021. doi:10.1001/jamacardio.2021.0185
Ultranomics marketing material discussing the “Novel Biomarkers”: https://www.ultromics.com/literature/coronary-artery-disease-prediction-from-resting-echocardiograms-using-novel-imaging-biomarkers
Chapter 5 – Chest/Lung
Article discussing ML algorithms superiority at seeing radiologist blind spots:
https://pubs.rsna.org/doi/10.1148/ryct.2020190222
Multimodal PE algorithm
https://www.nature.com/articles/s41598-020-78888-w
Chapter 6 – Body Imaging
FerriSmart website
https://ferriscan.com/ferrismart/
Chapter 7 – Mammography
DREAM Challenge Website
2019 SBI/ACR Breast Imaging Symposium: Deep learning for clinical practice: improving breast MRI workflow with automated tumor detection
https://www.eventscribe.com/2019/SBI-ACR/fsPopup.asp?Mode=presInfo&PresentationID=509987
Chapter 8 – IR
IR Chatbot
Loop X and Robot for IR
Ultrasound AMCAD BIOMed
https://www.amcadbiomed.com/product/ut
Chapter 9 – Marketplaces and Distribution
FDA AI ML Learning Paper
Leading the industry with 6 FDA cleared AI products, embedded into imaging modalities, PACS and RIS
The software package is available for GE Healthcare’s fixed and mobile X-ray and fluoroscopy hardware
https://www.fiercebiotech.com/medtech/ge-healthcare-taps-lunit-s-ai-for-its-new-chest-x-ray-suite
Siemens Healthineers uses artificial intelligence to take X-ray diagnostics to a new level
https://www.siemens-healthineers.com/press-room/press-releases/ysioxpree-ai-chest.html
Radiology image-processing firm 3DR Labs said it will include in its reports liver iron concentration data obtained using imaging laboratory services provider Resonance Health’s artificial intelligence (AI) tool for analyzing MR images.
https://www.auntminnie.com/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=128034
App in vRAD workflow
vRad Results App Apk Download for Android & iOS phones
Chapter 10 – Systemic Quality
How we successfully put AI models to work for our radiologists
https://blog.vrad.com/how-we-successfully-put-ai-models-to-work-for-our-radiologists
New Radiology AI Models Reduce Time to Care
https://blog.vrad.com/new-ai-models-reduce-time-to-care
Harnessing AI to ‘Make it Easier for Radiologists to Practice Better’
Artificial Intelligence: A Private Practice Perspective
https://www.jacr.org/article/S1546-1440(20)30976-5/pdf
How Artificial Intelligence Can Help In Image Transmission of Images
Denial Management with AI
https://www.changehealthcare.com/insights/denial-management-with-artificial-intelligence
Nuance Communications : Digital Front Door, Ambient Technology, AI, and The Revenue Cycle
Q&A: vRad’s CIO on AI beyond worklist prioritization, new solutions for radiologists, COVID-19 and more
AI Scheduling Platforms
https://www.lightning-bolt.com/ai-technology/
Icometrix year in review
https://www.linkedin.com/pulse/2020-year-like-other-wim-van-hecke/
Chapter 11 – Next Steps
Medical imaging market statistics:
ACR DSI FDA Cleared Algorithms
https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms
And
Charles Kahn Gamuts
https://www.gamuts.net/about.php
A Radiologist Introduction to AI and Machine Learning