Details

Autor(en) / Beteiligte

• Rousta, Fatemeh
• Esteki, Ali
• shalbaf, Ahmad
• Sadeghi, Amir
• Moghadam, Pardis Ketabi
• Voshagh, Ardalan

Titel

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review

Ist Teil von

• Computer methods and programs in biomedicine, 2024-06, Vol.250, p.108205, Article 108205

Ort / Verlag

Ireland: Elsevier B.V

Erscheinungsjahr

2024

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• •Artificial intelligence (AI), which includes classical machine learning (ML) and deep learning (DL), offers potential ways to enhance the performance of endoscopic ultrasound (EUS) regardless of the skill level of the operator.•AI is crucial in detection, classification, and segmentation of pancreatic lesions and stations.•This systematic review thoroughly examines the rapidly developing topic of AI-assisted systems in pancreas EUS, with the goal of clarifying the current state of research and progress.•The review emphasizes the difficulties involved with AI-assisted pancreatic EUS imaging, while also stressing the potential of AI in overcoming these limitations.•This review presents potential areas for future investigation in the field of AI-assisted EUS systems, thereby making a valuable contribution to the continuous progress in pancreatic diagnoses and therapy. The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.