From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.

Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

Autoimmune Pancreatitis Presenting as Obstructive Jaundice Mimicking Pancreatic Cancer: A Case Report.

Autoimmune pancreatitis (AIP) is a rare form of chronic pancreatitis that can be misdiagnosed as pancreatic cancer due to similar clinical and radiological findings. In this case report, we present a 49-year-old male patient who presented with obstructive jaundice and was initially diagnosed with pancreatic cancer based on imaging findings. However, the lack of definitive parenchymal tissue in the biopsy raised suspicion for an alternative diagnosis, which led to further testing and ultimately the diagnosis of AIP. The use of endoscopic ultrasonography (EUS) and fine needle biopsy (FNB) helped to obtain a tissue diagnosis and rule out malignancy. The measurement of serum IgG4 levels further supported the diagnosis of AIP. The patient was treated with glucocorticoids and showed gradual improvement, ultimately recovering from AIP. This case highlights the importance of maintaining a high level of suspicion and considering AIP as a possible diagnosis when investigating cases that mimic pancreatic cancer. Early recognition and treatment with steroids can result in a favorable outcome for patients with AIP.