About Client

Pneumonia is one of the leading causes of child mortality across the globe. A report by UNICEF has revealed that a child succumbs to pneumonia somewhere in the world every 39 seconds. As alarming as this might be, what’s worse is that this disease is not only preventable, but also treatable with timely intervention. But for that to happen, there needs to be a more effective means of early identification and treatment of pneumonia. The accuracy of diagnosis of conventional methods like X-rays, pulse oximetry, and blood and sputum tests is limited. 

 

About client

Challenge

About client

Slow Diagnosis

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Low Accuracy of Diagnosis

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Lack of Qualified Radiologists

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Delay in Treatment

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Higher Mortality

Solutions

Deep-Learning Analytics

OSP’s solution leverages deep learning technology and machine learning models to help the system review a chest radiograph accurately. Moreover, the deep learning technology enables the system to learn from radiographic images and get better over time. It learns as it sees more images over time and is able to diagnose faster with greater accuracy.  

 

Establishing a Benchmark

Radiology images of the chest are accepted as data and converted into a specific format that is compatible with the system. The images vary from those of healthy people all the way through to people with advanced pulmonological problems like pneumonia, and everything in between. This offers a reliable benchmark of reference for the system to learn. 

 

Assessing Images

An advanced CNN model analyses each radiology image and determines if it is pneumonia. This is made possible because of the deep learning system mentioned above. By assessing several dozen, or even hundreds of images, the system makes up for the years of experience of a seasoned radiologist. 

 

Detection of Anomalies

The system detects small, localized opaque areas in a chest X-ray and highlights those using bounding boxes. A healthy pair of lungs would have a clear X-ray, but those of a person suffering from pneumonia will show anomalies like opaque areas. 

 

Identification of Anomalies

Based on the number of bounding boxes in a certain X-ray, an XML generator creates three different datasets to help with faster and more accurate identification of anomalies in the X-rays. This enables the radiologists who are relatively new to make accurate diagnoses. 

 

Self-Learning System

The deep learning and machine learning model analyses the processed X-ray images and generates a type of file called pickle file. This type of file makes it useful for the system to learn, or train itself to make further predictions faster and better. 

 

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Results

Result

Automated Diagnosis

Result

Faster Diagnosis

Result

Clinical Decision Support

Result

Improved Accuracy of Diagnosis

Result

Makes Up for Lack of Radiologists

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