Medicine is a field in constant evolution, where precision and speed in diagnosis are essential for patient care. In this context, multilayer neural networks have emerged as a revolutionary tool that has transformed the way medical data is analyzed and interpreted. Let’s focus on medical image analysis, disease diagnosis and medical signal processing.
Quantitative Analysis of Medical Images
- Ultrasound – Ultrasound image analysis is essential in fields such as cardiology, obstetrics and radiology. Traditionally, clinicians have relied on visual inspection and qualitative analysis of these images. However, this methodology has limitations in the early detection of subtle pathologies. Multilayer neural networks have revolutionized this field by allowing quantitative analysis. In the case of heart attack detection, for example, cardiac ultrasounds are digitized into pixel matrices and gray levels. Through training, the network is taught to identify subtle differences between normal and abnormal heart tissue. This is based on the analysis of textures, shapes and densities, which leads to accurate and early identification of cardiac pathologies.
- Magnetic Resonance – Magnetic resonance (MR) image analysis is essential for the segmentation of anatomical structures and the detection of brain, musculoskeletal, and other disorders. MRIs are used successfully in the identification of structures, such as blood vessels and soft tissues. Accurate segmentation of MR images is critical for visualization and medical diagnosis. Here, these types of networks play a vital role by training to analyze signal intensity and texture patterns in MR images, enabling precise segmentation of structures such as white matter, gray matter and cerebrospinal fluid.
- Nuclear Medicine – Medical images obtained through techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) offer crucial information for the diagnosis of diseases. MRI has been successfully applied in the diagnosis of Alzheimer’s disease, using PET images being trained to classify subjects as normal or suffering from Alzheimer’s. Based on the analysis of metabolic parameters that represent glucose metabolism in various regions of the brain, the results have shown that MRI achieves a diagnostic accuracy of 92%, surpassing standard statistical methods.
- Radiology – In radiology, MRIs have also found application in the interpretation of medical images, such as angiographies and mammograms. For example, in the case of coronary artery angiography, the network processes high-resolution images for the automatic detection of blood vessels using convolution networks that scan images that combine with the use of masks and classify central pixels as vessels or background. The results indicate that this technique can equal or surpass classical statistical methods, discriminant analysis, which highlights its effectiveness in the analysis of medical images.
Signal AnalysisMédicas
- Detection of Heart Diseases through Acoustic Analysis – Coronary artery disease is one of the leading causes of death worldwide. Early detection is essential to prevent risks. A promising approach is based on the acoustic analysis of sounds associated with turbulent blood flow in partially obstructed arteries. This method is effective in differentiating healthy subjects from those with heart disease. The sound signal is recorded in a sound-proof room and processed to obtain relevant parameters. These parameters are entered into an MRI along with clinical and demographic data to improve diagnosis.
- Detection of Immune Deficiencies and Allergies – MRIs have also been used to identify deficiencies in the immune system and to distinguish between allergic and pseudo-allergic reactions. In the first case, the networks are trained using lymphocyte metabolic parameters, allowing the classification of individuals as healthy or immunodeficient. In the second case, the complexity of differentiating between allergic and pseudoallergic reactions is addressed. This is achieved through a network with multiple classes, where the inputs are the concentrations of lymphocyte metabolic parameters.
Conclusions
The applications of MRI in medicine are constantly expanding and improving, offering promising results in many areas, from the analysis of medical images to the detection of diseases and the diagnosis of disorders. The ability of these networks to learn and recognize complex patterns in medical data provides powerful tools for clinical decision making. As research and development continues in this field, it is expected that applications of neural networks in medicine will continue to provide additional benefits to patients and healthcare professionals, improving the accuracy and effectiveness of medical diagnoses.
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