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Product:

Version 3.0

qXR

Company HQ: 

Mumbai, India

Last Updated:

August 31st, 2020

qXR is an automated chest X-ray interpretation tool that detects 29 different findings, including signs of pulmonary, hilar, and pleural tuberculosis (TB). The artificial intelligence algorithm underlying qXR is trained to detect not only classical primary pulmonary TB, but also atypical manifestations. It can be used to simultaneously screen for chronic obstructive pulmonary disease, lung malignancies in high-risk populations, and certain cardiac disorders.

qXR can automatically fetch a chest X-ray from the X-ray machine or PACS and process it offline on a customized solution or online on a HIPAA compliant cloud service. It scans the chest X-ray for the presence of 29 different findings and generates a report indicating the presence of each of the findings, and 2 results indicating the likelihood of TB. It also marks the scan with the location of the abnormalities. It produces a yes/no output as well as a probability score of the confidence of the deep learning model for the particular finding.

qXR comes bundled with a complete case finding web-based software that helps track clients, monitor presumptives and log microbiological confirmatory results across different sites. The software can be accessed with any browser and includes a free radiology viewer that can be used to see the chest X-ray scans and qXR results.

Certification

CE-marked

Development Stage

On the market

Deployment

Online & offline

Intended Age Group

6+ years

Target Setting

Primary health centers, general hospital (above primary level), teleradiology companies, government/public sector, e.g. national TB program, private sector

Current Market

Global, qXR is being used in over 20 countries

Input

»» Can be used to read images from any kind of chest X-ray machine (vendor neutral)

»» Chest X-ray image format: JPEG, PNG, DICOM

»» Chest X-ray type: posterior-anterior chest X-ray, anterior-posterior chest X-ray, portable

»» Other requirements: none

Output

Structured report including: 

»» Probability score as well as dichotomous output indicating whether each abnormality is present or absent. 

»» Probability score for TB as well as dichotomous output indicating whether TB is likely present or likely absent. 

»» A box indicating the location of the abnormalities. 

Abnormalities detected by the product for which a separate abnormality score is given include: abnormal, tuberculosis, opacities (atelectasis, cavities, calcification, consolidation, fibrosis, nodules, reticulo-nodular pattern), emphysema/ hyperinflation, pleural effusion, blunted costophrenic angle, pneumothorax, cardiomegaly, tracheal shift, degenerative spine changes, scoliosis, hilar prominence, rib fractures, COVID-19, pneumoperitoneum, mediastinal widening, elevated hemidiaphragm, abnormal diaphragm shape, lines and tubes (4 types).

Server

Please contact partner@qure.ai for information.

Integration

It is possible to integrate the product with the client’s legacy Picture Archiving and Communication System (PACS).

Software

Ubuntu 18.04 is preferred

Processing Time

Less than a minute

Hardware

Please contact partner@qure.ai for information.

Validation

qXR can generalize to any X-ray manufacturer (CR/DR) and has been tested with over 20 leading X-ray manufacturers globally.

Data Sharing & Privacy

»» Server Location (for online product): the server can be set up in any location. location (Preferred is AWS, but the software can be deployed on any cloud provider). Set-up of a local or national server possible

»» The online deployment can be using a recommended cloud in-country or with a qure.ai preferred partner. The offline deployment is provisioned using a small footprint portable computer

»» No data is shared with the developer

»» All data is anonymized before analysis by qXR

Software Updates

»» Routine product upgrades for improvements and periodic updates for improved accuracy (annual)

Price

»» Please contact partner@qure.ai for information

Product Development Method

qXR uses deep learning to analyse chest X-ray scans. Read about algorithm development at blog.qure.ai or reach out to partner@qure.ai for details.

Training

The product was trained on 3,500,000 chest X-rays from all over the world

Reference Standard

Human readers, culture, sputum smear microscopy and GeneXpert results

Publications

1. Qin ZZ, Ahmed S, Sarker M S, Paul K, Adel A S S, Naheyan T, Banu S, Creswell J. Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest x-ray? A multiplatform evaluation of five AI products used for TB screening in a high TB-burden setting. https://arxiv.org/abs/2006.05509


2. Engle E, Gabrielian A, Long A, Hurt DE, Rosenthal A (2020) Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis. PLOS ONE 15(1): e0224445. https://doi.org/10.1371/journal.pone.0224445


3. Nash M, Kadavigere R, Andrade J, Sukumar CA, Chawla K, Shenoy VP, et al. Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India. Sci Rep. 2020 Dec 1;10(1):1–10. https://www.nature.com/articles/s41598-019-56589-3?proof=true


4. Qin ZZ, Sander MS, Rai B, Titahong CN, Sudrungrot S, Laah SN, et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019 Dec 1;9(1):1–10. https://www.nature.com/articles/s41598-019-51503-3


5. Putha P, Tadepalli M, Reddy B, Raj T, Chiramal JA, Govil S, et al. Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays. 2018 Jul 19 [cited 2020 Mar 30]; Available from: http://arxiv.org/abs/1807.07455


6. Singh R, Kalra MK, Nitiwarangkul C, Patti JA, Homayounieh F, Padole A, et al. Deep learning in chest radiography: Detection of findings and presence of change. Eapen GA, editor. PLoS One [Internet]. 2018 Oct 4 [cited 2020 Mar 30];13(10):e0204155. Available from: https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0204155

A complete list of publications can be found on https://qure.ai/publications.html

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