R A D I O LO G Y 1 5 Artificial intelligene technology continuously evolves AI value in breast screening Although breast cancer (BC) mammography screening enables early detection of breast cancer, mammography presents issues such as variability between the radiology readings and short- age of radiologists. This area of medical imaging is where artificial intelligence (AI) could help make the biggest difference and improve patient outcome, a Netherlands-based researcher told delegates at an April meet- ing in Spain. Report: Mélisande Rouger Millions of mammograms need to be read annually, putting a strain on radiology services. Computer aided diagnosis (CAD) emerged more than 20 years ago for second read- ings, with markers that highlighted the area of evaluation for further Peer-reviewed journals in which Transpara was independently validated, both used standalone or combined with radiologists. Education and management olution or reviews. The idea is to highlight the most relevant teaching cases to the current case, and then make the link with an encyclopaedia or radi- www.healthcare-in-europe.com Transpara, the AI software for 2-D and 3-D mammography, developed at ScreenPoint. The 2-D version has received CE and FDA approval. inspection. Overall, the benefit of using CAD is disappointing, Albert Gubern-Merida, an AI researcher in Nijmegen, explained at the ESR AI Premium meeting. ‘These systems all target the perception level and just help the radiologist double check things that could have been missed. Besides, those algorithms are out- dated,’ he pointed out. AI systems today are trained on millions of images and associated data. ‘Mammography is the best area of application because thousands of mammograms are generated daily around the world and need to be read,’ said Gubern-Merida, Head of Research and Development at ScreenPoint, a company with com- mercially available AI software for 2D and 3D mammography. Performance of some current AI systems is at least equal to human performance, research published in the Journal of National Cancer Institute suggests. At Radboud University Medical Centre, research- ers independently tested Transpara software vs. 101 radiologists, collect- ing both multiple centre and multi- vendor data sets. They showed that the algorithm was performing as well as the radiologists’ [Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute, 2019 djy222.] Machines will not replace radiologists Equal performance does not mean machines will eventually replace radiologists. ‘It’s not going to hap- pen, but we need to use this clini- cally proven, high quality technol- ogy to improve the care we give to women,’ Gubern-Merida empha- sised. There are various ways to apply ology assistant to broaden knowl- edge. It will be key to understand why AI systems think which cases are rel- evant, Kotter pointed out. ‘Usually, deep learning is a black box and you don’t know how it works. But, in order to learn how AI sys- tems work, AI systems must become explainable.’ One way could be to measure similar cases from an exist- ing database. Run in the background, AI could help radiologists detect gaps in their reading and recommend per- sonalised strategies – for example, read more cases for gastroenterol- ogy tumours. The impact on management There are many staff shortages com- pared to the high image data vol- ume to interpret. Time-consuming radiology services are increasing, fuelled by sub-specialisation and 24/7 services needed. Many promis- ing solutions are available, accord- ing to Professor Christoph Becker, at the Radiology Department, Geneva University Hospital. ‘Automation of our departmental workflow is probably the lowest hanging fruit to help us. But there is also automation of time consuming and repetitive visual tasks, particu- larly those with high volumes and low complexity. Automation of data management will help us to extract data from patient records, so that we don’t have to do it manually and may be able, in the future, to access the current scientific literature data mining related to the cases we are reading,’ he said. However, stopping radiology resi- dency programs now would have unknown consequences. ‘We sup- ply the radiologists for Western Switzerland and have 11 subspecial- ised units just in our department. This would deplete rapidly if we did not replace older radiologists who retire,’ Becker reasoned. Should training stop, many unsolved questions would arise, such as where and when to buy machines and robots, ideally with board certification and ESR level 3 subspecialty skills? Who would override the wrong decisions in the meantime? Are machines and robots acceptable as imaging consultants for difficult, complex cases? What would patients say? Who would take the medico- legal responsibility – robots or firms that supply them? Anxiety about potential future dis- placement is discouraging medical students to choose radiology as a career, a Canadian survey shows. ‘We must address those fears,’ said Becker, who outlined five immuta- ble elements to manage complex change: vision, skills, incentives, these technologies, which can improve clinical performance work- flow, especially in detection and decision support, recent research suggests [Detection of breast can- cer using mammography: Impact of an Artificial Intelligence support system. Radiology. 2019 290:2, 305- 314.]. ‘AI tools can highlight things that shouldn’t be missed without interrupting the reader; or, AI results can simply be requested when a second opinion is needed. Studies show that radiologists improve both specificity and sensitivity using these algorithms,’ he said. AI may positively impact on work- flow by reducing radiologists’ work- load. BC mammography screen- ing means reading many images. Second reading improves detection, but this approach might be unsus- tainable with the introduction of Digital Breast Tomosynthesis, since the reading time increases twofold compared to 2-D mammography. Having an AI system that immedi- ately helps to differentiate exams with and without suspicious lesions could help save a lot of time and trouble. ‘AI systems can produce a score, to detect and determine the risk of cancer presence in an exam. Usually there’s a score from 1-10 or 1-100,.’ he explained. One simple application, possi- ble today, is through the work list, by sorting and labelling exams by their risk of having a cancer – for instance, from the most likely cases to more likely ones, less likely ones, etc. and dividing tasks based on a radiologist’s schedule. AI should make decisions With an ever-thinning radiology workforce, he suggested, ‘Let AI decide if a second radiologist is needed to read a scan. AI systems are exceptionally good at reading normal scans, so let AI do that.’ Questions from users and ongo- ing dialogue with PACS providers are essential to ensure they are resources and an action plan. ‘If vision is lacking, confusion will result. If skills are lacking in your department, there will be anxiety. Lack of incentives will cause resist- ance in staff. Resources may be missing and cause frustration. And, if you have no action plan, or it’s not clear, downfalls are guaranteed.’ To integrate AI smoothly, people must decide what to automate and if it makes sense for their institu- tion. ‘A fool with a tool is still a fool. Besides, when is a tool mature for clinical routine? One must design a roadmap and steps to take. What cannot be measured cannot be man- aged,’ Kotter emphasised. Radiologists have lived with disruptive change for decades. Changing job descriptions have been the norm and technological progress demands constant updat- ing of skills. Rads have also been pioneers in many developments, including PACS integration – which almost never happened. ‘We’re used to revolutions in imag- ing. PACS was a revolution. We had many meetings about it and it took 20 years to become clinical routine,’ Kotter said, concluding: There’s also a Valley of Death of radiology, and many projects don’t make it to the work place.’ Dr Gubern-Mérida gained his joint PhD degree in medical imaging at the Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Centre in Nijmegen, the Netherlands and the University of Girona in Spain. His research focused on automated analysis of breast MRI, by developing image analysis and machine learning algorithms to detect breast cancer. As a post- doctoral researcher at DIAG he expanded his research to other breast imaging modalities (mammography, digital breast tomosynthesis, and automated 3-D breast ultrasound). In 2016, he joined ScreenPoint Medical, and currently heads Research and Development, focusing on the continuous evolution of AI technology. appropriate and compatible with a customer’s clinical practice. The main questions he said users should ask are: ‘How and where will I use these tools in my workplace?’ ‘Are these tools clinically relevant for my population and clinical images.’ ‘In mammography, we know the images aspect might change, given the different characteristics of devic- es from different vendors (i.e. detec- tor, angle, processing algorithms, etc.). An algorithm might suffer from these changes.’ Gubern-Merida has no doubt that BC screening is where AI matters. ‘It’s where women can benefit the most,’ he concluded. ‘AI tools are already there to help radiologists in clinical practice. But, we need more studies to validate the best approach to obtain the best out of it.’ Elmar Kotter MD MSc gained his Medicine and Computer Science degrees at Montpellier University (France) and Université René Descartes in Paris. From 1993-2000 he was radiology resident in Freiburg University Hospital, Germany, where, from 1994, he directed the Freiburg PACS-Project. From 1997 he headed IT in the radiology department and became its vice chairman in 2003. and, in 2008, became Associate Professor of Radiology at the University. He chaired the IT working group of Germany’s society of radiology (2006 - 2015). He is also vice-president of the European Society of Medical Imaging Informatics (EuSoMII), as well as a member of the Subcommittee on eHealth and Informatics for the European Society of Radiology.