Deploying automated machine learning for computer vision projects: a brief introduction for endoscopists.

Area of Science:

• Medical technology
• Artificial intelligence
• Endoscopy

Background:

• Computer vision is increasingly vital in medical imaging analysis.
• Endoscopic procedures generate vast amounts of visual data.
• Manual analysis of endoscopic videos is time-consuming and prone to error.

Purpose of the Study:

• To provide endoscopists with a foundational understanding of automated machine learning (AutoML).
• To highlight the potential of AutoML in enhancing computer vision applications within endoscopy.
• To demonstrate the practical deployment of AutoML for endoscopic image and video analysis.

Main Methods:

• Introduction to AutoML concepts and workflows.
• Demonstration of AutoML tools for image classification and object detection.
• Case examples of AutoML implementation in simulated endoscopic scenarios.

Main Results:

• AutoML offers a viable approach to automate complex computer vision tasks in endoscopy.
• Potential for improved efficiency and accuracy in endoscopic image analysis.
• Accessible tools enable endoscopists to leverage AI without extensive programming knowledge.

Conclusions:

• AutoML deployment can significantly benefit computer vision projects in endoscopy.
• Empowering endoscopists with AI tools can lead to advancements in diagnostic capabilities.
• Further exploration and adoption of AutoML are recommended for modern endoscopic practice.