Using Machine Learning to Diagnose Chest Xrays and Interpret Patient Symptoms and Medical History

Rohan Bhansali Rohan Bhansali

doi:10.17577/ijertv9is110163

Abstract

Using Machine Learning to Diagnose Chest Xrays and Interpret Patient Symptoms and Medical History - written by Rohan Bhansali published on 2020/11/26 download full article with reference data and citations

Highlights

Chest X-rays are the most frequently used medical imaging procedure and contain among the most significant and perilous diseases
This problem is further exacerbated in poor countries such as Rwanda, where eleven radiologists care for twelve million inhabitants, and Liberia, where, despite a population of four million, there are merely two practicing radiologists [10]
The cardiopulmonary diseases that are typically detected through these radiographs tend to be among the most lethal; they include pneumonia, a contagion that hospitalizes over a million Americans annually, of which approximately fifty thousand expire [14]; tuberculosis, which currently afflicts one fourth of the world’s population and kills an annual average of 1.3 million people worldwide [3]; and lung carcinoma, the deadliest cancer for both men and women, with over one hundred and fifty thousand annual deaths attributed to the disease [13]

Summary

Rohan Bhansali

Abstract— Chest X-rays are the most frequently used medical imaging procedure and contain among the most significant and perilous diseases Hospitals, especially those that are understaffed or have underqualified radiologists, would benefit greatly from an automated method of diagnosing these X-rays, which would drastically lower healthcare costs as well. The model would need to account for patient information, including symptoms and medical history The importance of these factors in the diagnosis can be highlighted by the example of a patient’s chest X-ray showing signs of congested lung vasculature. The model took a frontal X-ray as an input and outputted a vector of disease probabilities and a heat map of where the findings of the radiograph were localized Another prominent dataset is MIMIC-CXR, the largest collection of thoracic radiographs released to date [10].

Pleural thickening

Findings

CONCLUSION