Automated X-Ray Reads for Better Medical Diagnosis

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Wavelabs Insight uses deep learning technology to automate the chest X-ray interpretation process. When used as a point-of-care screening tool, followed by immediate bacteriological/NAAT confirmation, our framework can help significantly reduce time to confirmed diagnosis for tuberculosis and other lung diseases.


Data Pipeline

Wavelabs Insight can detect and localize multiple findings in a chest x-ray including abnormal classification, different types of lung parenchymal opacities, pneumothorax, pleural effusion, cardiac enlargement, and anatomical variations seen in the chest. It can be used to simultaneously screen for COPD, lung malignancies in high-risk populations, and certain cardiac disorders. The algorithms are trained and tested using a growing database of X-rays from diverse sources.

Training Pipeline

Designed for use in a real-world setting, Wavelabs Insight algorithms are hardware- agnostic and work with X-rays of varying quality and exposure, from all X-ray machine models. The framework can be trained to screen for all chest abnormalities.


Inference Pipeline

The model generates a description of the X-ray findings, including name, size and location of the abnormality, that is used to pre-fill radiology reports. It can easily be integrated within any workflow management platform to register and track patients through the process of clinical and X-ray screening, and sputum confirmation. The model can provide an overview of all patients registered for X-ray screening, bacteriological test results and radiology reports.

Monitoring and Logging

Wavelabs Insight also provides for real time quantification and progression monitoring services within its framework. It provides for fully automated detection, quantification and visualization of key metrics at each step of the process.



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