R A D I O LO G Y 1 5 Radiology and radiologists: a painful divorce The huge impact of AI Artificial intelligence based applications will replace radiolo- gists in some areas, the physicist Bram van Ginneken predicts. Michael Krassnitzer reports profoundly,’ ‘The profession of radiologist will change predicts Gram van Ginneken, Professor of Medical Image Analysis at Radboud University Medical Centre. The cause is automatic image analysis by com- puters (first published in a paper in 1963) and deep learning, the meth- od with which a computer learns to analyse images not by features extracted by a radiologist, but direct- ly from the images themselves. The title of his talk during the European Congress of Radiology (ECR 2018) in Vienna speaks volumes: ‘Artificial intelligence and radiology: a perfect match. Radiology and radiologists: a painful divorce?’ For 50 years scientists had tried to build a system that could automati- cally analyse medical images. They failed. Humans are better. However, in 2013 they discovered that deep learning, an old idea from the 1970s, suddenly worked, thanks to better computers, more data to train on and improved algorithms to ‘edu- cate’ artificial neural networks (the basis of deep learning). Currently, anything a human can do in one second, deep learning can also do. ‘Take any classification or detec- tion task for which a human expert looks at an image for one second, or scrolls through a scan (and prior scan) for a few seconds, and then knows the answers – that task can also be done by a computer using deep learning at, or above, the level of the human expert. It’s still a lot of work to collect a well-annotated large data set and to engineer the deep learning system to perform well – but, it can be done.’ AI-based applications In Ginneken’s view there are three types of applications: those replac- ing a radiologist’s task, those help- ing the radiologist with a task, and those doing something a radi- ologist does not do today – and probably never will. The applica- Editor-in-Chief: Brenda Marsh Art Director: Olaf Skrober Editorial team: Wolfgang Behrends, Sascha Keutel, Lena Petzold, Marcel Rasch Senior Writer: John Brosky Executive Director: Daniela Zimmermann Founded by Heinz-Jürgen Witzke ISSN 0942-9085 Correspondents Austria: Michael Kraßnitzer, Christian Pruszinsky.China: Nat Whitney France: Jane MacDougall. Germany: Anja Behringer, Eva Britsch, Annette Bus, Walter Depner, Cornelia Wels-Maug, www.healthcare-in-europe.com Bram van Ginneken PhD is Professor of Medical Image Analysis at Radboud University Medical Centre and has co-chaired the Diagnostic Image Analysis Group since 2010. He also works for Fraunhofer Institute for Medical Image Computing (MEVIS) in Bremen, Germany, and is a founder of Thirona, a company that develops software and provides services for medical image analysis. Bram studied Physics at Eindhoven University of Technology and Utrecht University. In 2001, he gained his doc- torate at the Image Sciences Institute on Computer-Aided Diagnosis in Chest Radiography, and he has (co-) authored close to 200 publications in leading international journals. m o c . k c o t s r e t t u h S / i a h c i h t t u w a r u t a J l o d a w u P : e c r u o S cine where new imaging modalities are introduced.’ That situation does not sadden Ginneken. ‘Don’t think about radiology or jobs for radiolo- gists,’ he says. ‘Think about the posi- tive impact AI intelligence will have on healthcare, globally.’ Promising radiomics for breast MRI Continued from page 12 imaging data, which could otherwise only be achieved through genom- ics or proteomics – i.e. exactly what Kaiser is envisaging with regards to breast MRI. ‘We must,’ he said, ‘extract as much prognostic information from breast MRI images as possible.’ This is also not as easy as it sounds. The automated evaluation of image databases still faces obstacles. A very essential one is the lack of standardised data: ‘Standardisation is the big challenge,’ the radiologist emphasised. As long as the data is not standardised because each facility and institute carry out investigations in their own individual way, it can- not automatically be combed through for information. ‘We must quantify the data,’ Kaiser stressed. How might that be achieved? Kaiser envisions a simple, solid decision tree, such as the one published by radiologists at the Medical University of Vienna around four years ago to differenti- ate between benign and malignant lesions in breast MRI. ‘This tool has a proven accuracy of 88% for the dis- tinction between benign and malig- nant tumours and helps to achieve a definitive diagnosis for a third of all lesions.’ If examinations were always carried out based on these decision trees this would result not only in a consist- ently improved precision and stand- ardisation of a diagnosis but also in quantified examination data. ‘This,’ he believes, ‘would be an opportunity to obtain comparable and valid data that could deliver prognostic information via radiomics.’ STRESS can be so relaxing. Accutron® MR3! www.i-love-stress.com i s t T h e s p e c i a l f o r S t r e s s - M R I ! Integrated infusion pump! Wireless & 3-Tesla-capable! Innovation – Made in Germany! Hauptstr. 255 66128 Saarbruecken www.medtron.com Contrast medium injectors and consumables for CT, MRI and angiography A software Ginneken co-developed is designed to help (non-expert) readers detect tuberculosis in chest X-rays tion BoneXpert automatically meas- ures bone age from a child’s hand X-ray, thereby replacing a human. ‘This program delivers a precise and standardised reading, so that 50 per- cent of radiologists no longer look at the images’, the Dutch physicist points out. Detecting TB in X-rays Another application of this type, CAD4TB (Computer-Aided Detect- ion for Tuberculosis) was co- developed by Ginneken. This is a software designed to help (non- expert) readers detect tuberculosis in chest X-rays. ‘The first version was released in 2011 and immedi- ately used in Zambia, even before it was approved,’ Ginneken says. Meanwhile, CAD4TB is used in 24 other poor countries where radiolo- gists are in short supply. An example for an AI-based application assisting radiologists is Veolity, a lung screening worksta- tion to read chest CT images effi- ciently. It includes automatic detec- tion of nodules, automatic propa- gation of nodules found on prior scans, volumetric segmentation of solid, non-solid and part-solid nod- Holger Zorn. Great Britain: Brenda Marsh, Mark Nicholls. Malta: Moira Mizzi. Spain: Mélisande Rouger, Eduardo de la Sota. The Netherlands: Madeleine van de Wouw. USA: Cynthia E. Keen, i.t. Communications, Lisa Chamoff. Subscriptions Liane Kaiser, Theodor-Althoff-Str. 45, 45133 Essen, Germany Subscription rate 6 issues: 42 Euro, Single copy: 7 Euro. Send order and cheque to: European Hospital Subscription Dept Printed by: WVD, Möhrfelden, Germany Publication frequency: bi-monthly Representatives China & Hongkong: Gavin Hua, Sun China Media Co, Ltd. Phone: +86-0755-81 324 036 E-Mail: gavin_hua@163.com Germany, Austria, Switzerland: Ralf Mateblowski Phone: +49 6735 912 993, E-Mail: rm@european-hospital.com ules, with a single click, automatic lobe assignment and nodule type assessment, emphysema scoring and coronary calcium scoring. ‘With this software, radiologists are about 43 percent faster,’ Ginneken explains. An example of the third type of app is StratX, a cloud-based quan- titative CT analysis service that sup- ports endobronchial valve (EBV) patient selection and therapy choice by providing clinically validated information on emphysema destruc- tion, fissure completeness and lobar volumes. This system enables assessment of all potentially suitable patients for that minimally-invasive treatment for severe emphysema. ‘Radiologists have never done that,’ Ginneken points out. Should physicians take over? In terms of these automated services a question arises: Why involve a radi- ologist at all? Why don’t the treating physicians take it on? Ginneken mentions optical coherence tomog- raphy (OCT), an ophthalmology imaging technique that gives a very detailed view of the retina. 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