Percutaneous sequential closure of an Abernethy malformation: A case report.

Abernethy malformation (congenital extrahepatic portosystemic shunt [CEPS]) is rare and is characterized by an aberrant connection between the portal and systemic veins, bypassing the liver. It can have varying presentations and can lead to severe complications if left untreated. It is usually diagnosed incidentally on abdominal imaging. Occlusion venography and measurement of portal pressures (pre- and postocclusion) is an important step in management. Complete occlusion of the malformation in cases where the portal veins in the liver are very small and the gradient is more than 10 mm Hg, can potentially lead to acute portal hypertensive complications, such as porto-mesenteric thrombosis. We report a case of Abernethy malformation diagnosed on an abdominal computed tomography scan that presented with neurological symptoms and was successfully managed by interventional radiology via endovascular closure through placement and sequential occlusion of 2 metal stents.

The past, present and future role of artificial intelligence in imaging.

Artificial intelligence (AI) is already widely employed in various medical roles, and ongoing technological advances are encouraging more widespread use of AI in imaging. This is partly driven by the recognition of the significant frequency and clinical impact of human errors in radiology reporting, and the promise that AI can help improve the reliability as well the efficiency of imaging interpretation. AI in imaging was first envisioned in the 1960s, but initial attempts were limited by the technology of the day. It was the introduction of artificial neural networks and AI based computer aided detection (CAD) software in the 1980s that marked the advent of widespread integration of AI within radiology reporting. CAD is now routinely used in mammography, with consistent evidence of equivalent or improved lesion detection, with small increases in recall rates. Significant false positive rates remain a limitation for CAD, although these have markedly improved in the last decade. Other challenges include the difficulty clinicians encounter in trying to understand the reasoning of an AI system, which may limit their confidence in its advice, and a question mark hangs over who should be liable if CAD makes an error. The future integration of CAD with PACS promises the development of more comprehensively intelligent systems that can identify multiple, challenging diagnoses, and a move towards more individualised patient outcome predictions based upon AI analysis.