J-Net System: A New Paradigm for Artificial Neural Networks Applied to Diagnostic Imaging - Application

In this chapter we show how the ability of the J-Net system to extract the pictures composing it from an image, on the basis of the brightness, can have important medical applications. Two examples are shown: hidden arterial stenosis discovery in Digital Subtraction Angiography (DSA) and lung nodule characterization according to the benign or malignant feature.

First example: the popliteal artery is a relatively short vascular segment of the leg but is affected by a unique set of pathologic conditions. The most common of these conditions, is the narrowing or stenosis of the artery due to atherosclerosis. The clinical manifestations, imaging appearances, and treatment options associated with these pathologic conditions differ significantly. Consequently, the radiologist should be familiar with these conditions to direct imaging for accurate diagnosis and treatment and to prevent loss of limb. A standard DSA in a patient with popliteal artery stenosis showed just one stenosis, while after processing with J-Net, a second stenosis, which was invisible to human eye emerged bottom-up. At surgical intervention there was the confirmation that the patient was actually having another stenosis which was not visible at DSA.

Second example: the solitary lung nodule is a common radiological abnormality that is often detected incidentally. Although most solitary pulmonary nodules have benign causes, many represent early malignant lung cancers. Initial evaluation with Computed Tomography often results in non-specific findings, in which case nodules are classified as indeterminate and require further evaluation to exclude malignancy. In uncertain situations growth rate assessment remains still the only practical approach.

In order to verify this assumption we have used images published by a group of researchers in a top quality scientific journal regarding two cases in which only after two-three years it was possible to diagnose a malignant lung cancer, while at Time 0 the picture did not allow for an accurate and differential cancer diagnosis. J-Net applied to time 0 image was able to show how the image would have changed its pattern at time 1, delineating the cancer’s pattern of spread that would have occurred later on. J-Net seems to capture slight brightness intensities changes due to initial limphoangiogenesis spread driving microscopic peritumoral infiltration in malignant lung nodules.

Keywords: Image processing, Artificial Neural Networks, Active Connections Matrixes