Intelligent Imaging: Anatomy of Machine Learning and Deep Learning

Abstract

Complete the test online no later than December 2022. Your online test will be scored immediately. You may make 3 attempts to pass the test and must answer 80% of the questions correctly to receive 1.0 CEH (Continuing Education Hour) credit. SNMMI members will have their CEH credit added to their VOICE transcript automatically; nonmembers will be able to print out a CE certificate upon successfully completing the test. The online test is free to SNMMI members; nonmembers must pay $15.00 by credit card when logging onto the website to take the test. The emergence of artificial intelligence (AI) in nuclear medicine and radiology has been accompanied by AI commentators and experts predicting that AI would make radiologists, in particular, extinct. More realistic perspectives suggest significant changes will occur in medical practice. There is no escaping the disruptive technology associated with AI, neural networks, and deep learning, the most significant perhaps since the early days of Roentgen, Becquerel, and Curie. AI is an omen, but it need not be foreshadowing a negative event; rather, it is heralding great opportunity. The key to sustainability lies not in resisting AI but in having a deep understanding and exploiting the capabilities of AI in nuclear medicine while mastering those capabilities unique to the human resources.

Ethical principles for the application of artificial intelligence (AI) in nuclear medicine

“…While AI is not new in Nuclear Medicine, more recent developments and applications of machine learning and deep learning create refreshed interest in the ethical issues associated with AI implementation in Nuclear Medicine. Insight into the architecture, operation and implementation of AI in Nuclear Medicine is beyond the scope of this discussion and has been reported elsewhere (1)(2)(3). Nonetheless, it is important to provide key definitions.…”

“…AI is a general term used to describe algorithms designed for recognition, problem solving and reasoning generally associated with human intelligence, essentially imitating some aspects of intelligent human behaviour (1,3). An artificial neural network (ANN) is a subset of AI, and in medical imaging, an ANN is an image analysis algorithm composed of layers of connected nodes that simulate the neuronal connections of the human brain (2,3). ANNs are designed to analyse data and recognise trends or patterns that inform predictions (e.g.…”

“…classification of disease). A convolutional neural network (CNN) is a type of ANN used for deep learning that employs a convolutional process to extract features from the image itself, while an ANN typically has feature data as the input (1,2).…”

“…Machine learning (ML) is a subtype of AI that employs ML algorithms through data analysis without being explicitly programmed. ML tends to be associated with solving problems of logic after learning from human-defined teaching cases typical of an ANN (1,2). Deep learning (DL) is then a sub-type of ML that adds a number of processing layers (depth) to detect complex features in an image typical of a CNN (1, 2).…”

Dental Images Recognition Technology and Applications: A Literature Review

“…At present, one of the artificial intelligence methods employed in clinical fields is called deep learning [6]. Artificial intelligence is the term used to describe the algorithms designed for problem solving and reasoning [7]. The success of deep learning is mainly due to the progress in the computer capacity, the huge amount of data available and the development of algorithms [1].…”