AI/ML Articles & Videos

This article outlines the process of developing an AI/ML algorithm from scratch and getting it FDA cleared. It covers the four phases of the process (Explore, Develop, Validate, and Document) and discusses the costs, time, and data requirements involved. It also provides advice on regulatory strategy, data annotation, and algorithm prototyping. If you're interested in developing a medical device involving AI/ML.

What is the difference between user needs and requirements?

Learn how to avoid invalidating your test dataset while still allowing yourself some flexibility to re-use the test data.

This video discusses the potential avenues for getting generative AI applications cleared through FDA.

This article provides a questionnaire for those looking to get their AI algorithm FDA cleared. It covers topics such as validation studies, non-ML and ML software items, and acceptance criteria for the device. Reading this article can help uncover gaps that may need to be addressed before FDA submission.

This article outlines the challenges of bringing radiology AI/ML to market, from clinical workflow to IT installation. It provides insights and lessons learned for those interested in developing and deploying AI models in radiology.

FDA's Draft PCCP (Predetermined Change Control Plan) guidance should streamline model changes AI/ML-enabled devices, allowing for pre-approved modifications without the need for additional marketing submissions.

This webinar covers the four phases of the regulatory process: Explore, Develop, Validate, and Document. It also discusses the costs, time, and data requirements involved in the process. Additionally, it provides advice on regulatory strategy, data annotation, and algorithm prototyping.

This FDA guidance applies to AI/ML-enabled radiology software that assists in the detection of disease. Many of the principles and approaches, however, apply beyond this limited scope.

This guidance is describes FDA’s thoughts on evaluating the performance of CADe radiological devices. Designing standalone and clinical performance testing studies for AI/ML-enabled SaMD can be tricky. You want to do enough testing to ensure the device is safe and effective, yet you also need to avoid going bankrupt on the way!

This document outlines the technical performance assessment required for premarket submissions of quantitative imaging devices to the FDA. It provides guidance on the evaluation of the safety and effectiveness of these devices, including the methodology for quantitative imaging and the appropriate use of imaging biomarkers. Medical device manufacturers may be interested in reading this document to ensure their products meet the required standards and to understand the regulatory expectations for premarket submissions.

Introduces standardized terms and definitions to foster a consistent understanding across the industry for ML-enabled devices.

How can machine learning help us improve medical images for human viewers or image processors? This article describes one machine learning method (generative adversarial networks) that has been adopted by the medical imaging community to enhance medical images.

Want to know how we develop safe, effective, and FDA compliant machine learning algorithms? This article describes how we develop machine learning algorithms, points out common pitfalls, and makes documentation recommendations.

There are many approaches to choosing a medical imaging segmentation algorithm. In this article, we provide an overview of how to choose a neural network architecture for medical image segmentation.

Annotating medical images is time-consuming and expensive. In this article, we explain how self-supervised learning can stretch limited training data and compare it to transfer learning. We also explore three self-supervised learning medical imaging tasks.

Experienced radiologists can identify the anatomical location of an axial CT slice within a second. They may say the slice is “near the apex of the heart” or “at the C7 vertebrae.” These anatomical landmarks are difficult to describe or detect using manually created features, but neural networks excel at this sort of pattern recognition. Can we create a neural network capable of performing slice localization with similar speed and accuracy to a radiologist?

If you followed along with our last post, we developed a deep-learning model that achieves our goal of identifying Simpsons characters in an image. However, as with all software development tasks, getting a working program is only half the battle. In order to maintain a program and fix bugs, the developer must understand the system– in particular, they must understand how it fails as well as how it succeeds. This can be quite a difficult task for deep-learning models, as they are black-boxes by nature of their construction. However, there are some techniques we have at our disposal to open up the black box and get a view into what is happening in our trained model; these can help us to find “bugs” in the model’s learning and even indicate how to resolve them. Among the many techniques to visualize the internals of a deep learning model, we will be focusing on the use of class activation maps.