INTELLIGENT HEALTHCARE

Healthcare providers and payers can now benefit from AI medical imaging technology to screen for early signs of  chronic disease in large populations.

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enables more accurate risk-adjustment so patients are provided with preventative care paths they need, while improving quality of health services at significantly lower associated costs.

* Nanox AI solutions software based on medical imaging (CT or X-ray)

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AI solutions

Lighting Up New Pathways for Smart Population Healthcare Management

Nanox AI

solutions were developed to target highly prevalent chronic and acute diseases affecting large populations everywhere. Healthcare systems and payers aim to reap the benefits of early detection and intervention to improve patient care, while reducing costs and risk. Sadly, it is not possible to screen everyone for everything all the time. Using medical imaging (CT or X-ray) already available to the system, Nanox AI’s solutions help physicians identify the early signs.

 Bone
Bone
Cardiac
Cardiac
Reimbursement
Reimbursement
Triage
Triage

Bone Health Solution

Identification of vertebral compression fractures (VCF from CT images), an early sign of osteoporosis, in an effort to help curb the disease’s long-term impact.

 Bone

Cardiac Solution

Analysis of non-gated CT images for Coronary Calcium Detection quantifies coronary artery calcium calcification, and early warning for asymptomatic patients suffering from coronary artery disease.

Cardiac

Proper Reimbursement

Facilitating risk-adjusted reimbursement through value-based payment programs such as Medicare Advantage.

Reimbursement

Triage Solutions

A suite of radiological computer-aided triage and notification software indicated for use to aid with clinical assessment of Chest X-ray images for Pneumothorax (PNX) and Free Air (PPT), and non-contrast head CT images for Intracranial Hemorrhage (ICH).

Triage

Bone Health Solution

Identification of vertebral compression fractures (VCF), an early sign of osteoporosis, in an effort to help curb the disease’s long-term impact.

Nanox.Ai Bones solution

Cardiac Solution

Analysis of images for Coronary Calcium Detection (CCSng) quantifies coronary artery calcification, an early-warning for asymptomatic patients suffering from coronary artery disease.

Proper Reimbursement

Facilitating risk-adjusted reimbursement through value-based payment programs such as Medicare Advantage.

Nanox.Ai PROPER REIMBURSEMENT solution

Triage Solutions

A suite of radiological computer-aided triage and notification software indicated for use in analysis, including Pneumothorax (PNX) and Free Air (PPT), and Intracranial Hemorrhage (ICH).

TRIAGE SOLUTIONS
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CPT code

An Industry First CPT Code for Imaging AI Granted to Nanox.AI Bone Health Solution for Detection of Vertebral Compression Fractures to Identify Osteoporosis Patients 

In a landmark approval, the American Medical Association (AMA) issued a new CPT named "Cat III-Assistive Augmented Intelligence Analysis," and defined as an "Accepted addition of code 0X36T to report an automated analysis of an existing computed tomography study for vertebral fractures." 

This is a milestone towards smarter risk-adjustment of population healthcare plans—and potentially a game changer in the long term management of osteoporosis.  
*Effective January 1, 2022.

We are happy to announce issuance of a new Category III CPT® Code by the American Medical Association for our coronary artery calcium population health solution.

The new code establishes a reimbursement pathway for use of Nanox.AI’s HealthCCSng, an FDA-cleared AI-enabled cardiac imaging solution that detects coronary artery calcium (CAC), for patients in the U.S, promoting broader adoption of AI for improved population health.
*Code will become effective July 1, 2022.

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Recent Publications

1 articles
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Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization

January 2020

Authors

Noa Dagan, Eldad Elnekave, Noam Barda, Orna Bregman-Amitai, Amir Bar, Mila Orlovsky, Eitan Bachmat & Ran D. Balicer

Published in:

Nature Medicine volume 26, pages77–82(2020)
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PHT-BOT: Deep-Learning Based System For Automatic Risk Stratification Of COPD Patients Based Upon Signs Of Pulmonary Hypertension

October 2019

Authors

David Chettrit, Orna Bregman Amitai, MS, Itamar Tamir, Amir Bar, MS, Eldad Elnekave, MD

Presentation at:

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade

January 2019

Authors

Arun Krishnaraj, MD, MPH, Spencer Barrett, MD, Orna Bregman-Amitai, MSc, Michael Cohen-Sfady, PhD, Amir Bar, MSc, David Chettrit, MSc, Mila Orlovsky, MSc, Eldad Elnekave, MD

Published in:

TextRay: Mining Clinical Reports To Gain A Broad Understanding Of Chest X-Rays

October 2018

Authors

Jonathan Laserson, Christine Dan Lantsman, Michal Cohen-Sfady, Itamar Tamir, Eli Goz, Chen Brestel, Shir Bar, Maya Atar, Eldad Elnekave

Published in:

Improved Intracranial Hemorrhage Classification using Deep Multi-task Learning

October 2018

Authors

Amir Bar, MS; Michal Mauda, MD, PhD; Yoni Turner, MD; Michal Cohen-Sfady, PhD; Eldad Elnekave, MD

Published in:

Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement

October 2018

Authors

Philip Teare, Michael Fishman, Oshra Benzaquen, Eyal Toledano, Eldad Elnekave

Published in:

Journal of Digital Imaging, Volume 30, Issue 4

RadBot-CXR: Classification of Four Clinical Finding Categories in Chest X-Ray Using Deep Learning

October 2018

Authors

Chen Brestel, Ran Shadmi, Itamar Tamir, Michal Cohen-Sfaty, Eldad Elnekave

Published in:

Compression fractures detection on CT

October 2017

Authors

Amir Bar, Lior Wolf, Orna Bergman Amitai, Eyal Toledano, and Eldad Elnekave, The Blavatnik School of Computer Science, Tel Aviv University

Published in:

Language Generation with Recurrent Generative Adversarial Networks without Pre-training

October 2017

Authors

Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf

Published in:

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