Computer Aided Detection Helps Diagnose or Detect Several Serious and Even Life-Threatening Diseases

 

Computer aided detection software use pattern recognition methodology to assist physicians with the interpretation of medical images. It is a clinical tool that helps diagnose or detect many diseases such as cancer, neurological diseases, coronary artery disease, and cardiovascular diseases through imaging modalities such as computed tomography (CT), Magnetic Resonance Imaging (MRI), and X-Rays imaging. Computer aided detection software helps physicians to reduce detection errors. Moreover, it helps in accurately detecting chronic diseases at early stages, reducing mortality due to cancer, neurological and cardiovascular diseases.


 

The burden of cancer and heart disease is increasing worldwide. Cardiovascular diseases are the leading cause of death in Germany and Mexico, accounting for a total of around 40% and 20% of all deaths, respectively. Moreover, in Japan, it is estimated that 26.3% of men and 17.6% of women develop cancer before the age of 75. Computer aided detection software can help reduce the global disease burden by accurately detecting such serious and life-threatening diseases at early stages. It helps detect life-threatening diseases at early stages, reduce false-negative rates, improve inter and intra reader variability, and reduce mortality.

Moreover, computer aided detection software is widely used to help physicians in many tasks, including providing a second opinion on clinical and detection results and automatically recognizing all non-calcified lesions doubted of malignancy at images. By reducing the number of observations that lead to false negatives, this software can help eliminate the natural biases inherent in the diagnostic process and medical imaging. Computer aided detection has come a long way over the years, from the first computer aided drawing, which helped doctors draw conclusions based on research studies, to the current computer aided detection that helps physicians interpret medical images using a variety of technologies.

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