Artificial intelligence in abdomen radiology:

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics.

Aims:

The real value of AI lies in how it can be used in collaboration with the radiologist or medical professional. In how it can be used to enhance and support the professional by streamlining the process, reducing the diagnosis burden, and improving workflow efficiency. Artificial intelligence in radiology is a tool, not a sentient being. It is an investment into technology that allows for ongoing improvements to diagnosis and patient care by supporting the radiologist as they battle increasingly weighty workloads.

This is one of the field in which artificial intelligence research blossomed recently.

Methods:

There are two ways of using AI in radiology:

1. Programming an algorithm with predefined criteria supplied by experienced radiologists. These rules are hardwired into the software and enable it to perform straightforward clinical tasks.
2. Letting an algorithm learn from large volumes of data with either supervised/unsupervised techniques. The algorithm extracts patterns by itself and can come up with insights that escaped the human eye.

Open source research focus on those applications:

• Improve medical research by providing new tools to assess diagnostic radiology.
• Improve radiological interventions example coiling, cancer treatment and embolization.
• Improve planning of surgical interventions example segmenting blood supply to colon and rectum prior to undertake colorectal anastomosis.

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