Machine learning (ML) and artificial intelligence (AI) are widely applied in many disciplines including medicine. Pattern recognition or automatization has been successfully implemented in various field studies. Similarly, multiple efforts have been made in medicine to implement AI/ML technology to solve medical problems, for example, for automating osteoporosis detection. In general, the success of AI/ML technology is highly dependent on the amount of available data, especially during the training stage. Feature generation is a common technique that allows the manipulation of available data for the training stages. This paper aims to study the feasibility of adopting signal-processing techniques for feature generation in medical image processing. Signal attributes from signal processing workflow were adopted and applied to image processing of CT and DEXA scanning data to differentiate between normal and osteoporotic bone. Five attributes, namely amplitude, frequency, instantaneous phase, roughness, and first derivative or contrast attributes, have been tested. An attribute index number is formulated to indicate the attribute’s strength at the selected region of interest (ROI). A case study applying these attributes to the CNN model is presented. More than five hundred CT scan images of normal and osteoporosis bone were used during the training stage to test classification performance with and without developed attributes as an input. From the ten selected CT scan images used to test the CNN model, 90% were well predicted in the scenario only utilizing the grayscale as input. However, when including the developed attributes, the CNN can predict all the images well (100% were well predicted). In conclusion, the technique adopted from the signal-processing technique has the potential to enhance feature generation in image processing, whereby the results can be used for the early application of AI/ML in osteoporosis identification. Further research testing this proposed method in different image modalities needs to be conducted to verify the robustness of the proposed method.

The study presents the fluid-structure interaction (FSI) modeling in Peripheral Arterial Disease (PAD) geometry, highlighting the effects of arterial blockage on hemodynamics and arterial wall mechanics. Employing a RANS-based SST-Tran model, the study examines stenotic PAD models under realistic boundary conditions, coupled with a hyperelastic Mooney-Rivlin model to simulate the arterial wall's response. The analysis includes velocity profiles, wall shear stress (WSS), pressure distribution, and wall displacement, revealing significant differences between healthy and stenosed models. It demonstrates adaptive hemodynamics, the impact of stenosis on flow mechanics, and potential implications for atherosclerosis and plaque formation. The findings highlight the importance of understanding stenosis severity for clinical risk assessment, treatment planning, and monitoring.

Arteriovenous fistula (AVF) is an abnormal direct communication between an artery and the venous drainage without a capillary component. Although renal AVF is rare, detecting the disease is important to assist in management and, thus, help reverse the sequelae of the disease such as hypertension and heart failure. The clinical presentation and radiological features are important parameters that can help in the diagnosis and decision of treatment. We would like to share our successful approach to a 52-year-old female with idiopathic high-flow left renal AVF with concomitant renal artery aneurysm using a single Amplatzer vascular plug in the limit of our setting.

Analysis of Brain Activation and Effective Connectivity During Self-paced Unilateral and Bilateral Finger Tapping Using Functional Magnetic Resonance Imaging in Patients with Temporal Lobe Epilepsy - Gazi Medical Journal