Researchers at Duke University Medical Center have developed a deep-learning-based computer-aided detection (CAD) system to identify difficult-to-detect brain metastases on MR images. The algorithm exhibited excellent sensitivity and specificity, outperforming other CAD systems in development. The tool shows potential to enable earlier identification of emerging brain metastases, allowing them to be targeted with stereotactic radiosurgery (SRS) when they first appear and, for some patients, reducing the number of required treatments. SRS, which uses precisely focused photon beams to deliver a high dose of radiation to targets in the brain in a single radiotherapy session, is evolving into the standard-of-care treatment for patients with a limited number of brain metastases. To target a metastasis, however, it must first be identified on an MR image.

Using artificial intelligence, an international team of physicists has shown that the thousands of equations needed to model a complex system of interacting electrons can be reduced to just four. This was done by using machine learning to identify patterns previously hidden within the system of equations. The technique could be used to vastly reduce the effort required to calculate electronic properties, says the team, which was led by Domenico Di Sante at the University of Bologna, who is also a visiting research fellow at the Flatiron Institute in New York City. Quantum interactions between electrons underly the properties of matter, and over the past century physicists have developed mathematical and computational tools to boost our understanding of systems ranging from individual atoms to solid materials. These models must consider entanglement, a quantum phenomenon that allows stronger correlations between electrons than exists in classical physics.

Incorporating ventilation images into radiotherapy plans to treat lung cancer could reduce the incidence of debilitating radiation-induced lung injuries, such as radiation pneumonitis and radiation fibrosis. Specifically, ventilation imaging can be used to adapt radiation treatment plans to reduce the dose to high-functioning lung. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) scans are the gold standard of ventilation imaging. However, these modalities are not always readily available and the cost of such exams may be prohibitive. As such, researchers are investigating the feasibility of alternatives such as MR or CT ventilation imaging.

Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.

How much is too much? These are questions that cut to the heart of a complex issue currently preoccupying senior medical physicists when it comes to the training and continuing professional development (CPD) of the radiotherapy physics workforce. What's exercising management and educators specifically is the extent to which the core expertise and domain knowledge of radiotherapy physicists should evolve to reflect – and, in so doing, best support – the relentless progress of artificial intelligence (AI) and machine-learning technologies within the radiation oncology workflow. In an effort to bring a degree of clarity and consensus to the collective conversation, the ESTRO 2022 Annual Congress in Copenhagen last month featured a dedicated workshop session entitled "Every radiotherapy physicist should know about AI/machine learning…but how much?" With several hundred delegates packed into Room D5 at the Bella Center, speakers were tasked by the session moderators with defending a range of "optimum scenarios" to align the know-how of medical physicists versus emerging AI/machine-learning opportunities in the radiotherapy clinic.

Which innovations will have the greatest impact in radiotherapy by 2030? That was the question posed in the closing session of last week's ESTRO 2022 congress; and five experts stepped up to respond. As often seen in debate-style ESTRO sessions, competition was intense and gimmicks were plentiful, with all talk titles based on movies and a definite sci-fi twist. Before battle commenced, the audience voted for their preferred innovation based on the presentation titles. This opening vote put personalized inter-fraction adaptation as the winner.

Machine learning algorithms could one day be used to improve speech recognition in hearing-impaired people, researchers in Germany have shown. Using a novel algorithm, Jana Roßbach and colleagues at Carl von Ossietzky University could accurately predict when people with both normal hearing, and those with different levels of hearing impairment would mishear over 50% of words in a variety of noisy environments – an important test of hearing-aid efficacy. The lives of many hearing-impaired people have been significantly improved by hearing aid algorithms, which digitize and process sounds before delivering an amplified version into the ear. A key challenge still faced by this technology is improving the devices' ability to differentiate between human speech and background noise – something that is done using digital signal-processing algorithms. Researchers often use listening experiments to evaluate the ability of hearing aid algorithms to recognize speech.

Infrared night-vision systems that see in colour could be a reality thanks to researchers in the US, who have used machine learning to create colour images of photographs that are illuminated with just infrared light. The team hope their technique could be further developed to create imaging systems that operate where the use of visible light is impossible, such as retinal surgery. Traditional night vision systems work by illuminating an area with near infrared radiation and detecting the reflections or by using ultrasensitive cameras to detect the small amount of light present even at night. Both, however, usually produce monochromatic images, so researchers are seeking ways to produce multi-colour images of objects without having to bathe them in visible light. Computer scientist Pierre Baldi of University of California, Irvine (UCI), explains that this would be very useful in medical applications where use of visible light is problematic.

This episode of the Physics World Weekly podcast features an interview with the physicist Maria Schuld, who is a senior researcher and software developer at Xanadu – a Toronto-based quantum technology company. She talks about the challenges and rewards of implementing machine-learning systems on quantum computers. Also on hand is the author Kit Chapman, who chats about his latest book Racing Green: How Motorsport Science Can Change the World. He explains how the myriad technologies developed to make racing cars faster and safer have already benefitted society – and how they could help us combat climate change.

Artificial intelligence has potential to improve the operation of many essential tasks in various fields of medicine and biomedicine – from dealing with the massive amount of data generated by medical imaging, to understanding the evolution of cancer in the body, to helping design and optimize patient treatments. At last week's APS March Meeting, a dedicated focus session examined some of the latest medical applications of artificial intelligence and machine learning. Opening the session, Alison Deatsch from the University of Wisconsin, Madison, discussed the use of deep learning for diagnosing and monitoring brain disease. "Brain disorders and neurodegenerative disease are some of the most costly diseases, both in terms of human suffering and economic costs," she explained. The reason is that most of these conditions – which include Alzheimer's and Parkinson's disease, autism spectrum disorder and mild cognitive impairment (MCI), among others – lack reliable tools for diagnosis and progression monitoring and, as such, are often misdiagnosed.