Breast cancer is the most common cancer found in women worldwide, and one of the most effective ways to combat it is early detection through screening. However, this faces several challenges, such as limited access to screening, socio-economic issues and the difficulties in getting conclusive results from some tests. Taking screening closer to the people and employing artificial intelligence in diagnostics are two viable solutions that doctors are using to promote early detection.

Today, Siemens Healthineers Head of Marketing Women’s Health Aline Hambüchen talks with Dr. Brian Englander, Chairman of the Pennsylvania Hospital Department of Radiology, Federica Pediconi, professor of radiology at the University of Rome La Sapienza, Marco Caballo, Ph.D., a biomedical engineer and AI researcher at Radboud University Medical Center in Gelderland, NL and Abby Weldon, Senior Director of Women’s Health at Siemens Healthineers.

Listen to learn about what breast cancer screening entails and the barriers stopping women from being screened. You’ll also find out about mobile mammography trucks and the problems they are solving. Additionally, you’ll hear about the role that artificial intelligence (AI) can play in improving the diagnostic process.

What You’ll Learn in This Episode:

• How breast cancer gets diagnosed (02:57)
• Barriers to preventative breast cancer screening (05:16)
• How a mobile mammogram truck works ( 09:33)
• The impact that telemedicine is having on healthcare services (12:33)
• The consequences of canceled and delayed breast cancer screening (13:57)
• The role that AI can play in the fight against breast cancer (15:35)

Connect with Marco Caballo, Ph.D.:

• LinkedIn

Connect with Dr. Brian Englander:

• LinkedIn

Connect with Aline Hambüchen:

• LinkedIn

Connect with Abby Weldon:

• LinkedIn

Learn more about breast cancer prevention and self-examination here:

• https://www.siemens-healthineers.com/company/breastcancer-care

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The pursuit of precision medicine using technology has given rise to the idea of creating patients' digital twins. The successful development of digital twins of individual organs has set the stage for its development. However, the quantity and quality of data required to create a functional patient twin is an obstacle that is yet to be conquered, as are legislative and regulatory challenges.

Today, Peter Schardt, Siemens Healthineers’ Chief Technology Officer is joined by four guests - Dr. Ulrike Attenberger, Professor and Chair of the Department of Radiology at the University Hospital in Bonn, Germany. Tobias Heimann, Head of Artificial Intelligence at Siemens Healthineers Germany. Siobhan Graham, Head of Radiotherapy at Barking, Havering, and Redbridge University Hospitals in Essex, UK, and Nikki Akar, Lead Chemotherapy Nurse at the same hospital.

Stay tuned to find out what a patient twin is, how it can be created and the benefits it could offer patients and medical practitioners. You’ll learn more about the cloud-based software as a service, Noona, which can be seen as a first step towards a disease-focused version of the digital twin, and how it is used by cancer patients as their 24/7 companion on their journey.

What You’ll Learn in This Episode:

• How a digital twin of the liver is used in medical research (03:49)
• How digital twinning can help advance precision medicine (04:45)
• The requirements for building a patient’s twin (07:52)
• How Noona is used at Barking, Havering, and Redbridge University Hospitals in Essex, UK (10:44)
• The impact that Noona is having on the provider-patient relationship (13:56)
• What would need to be done to collect enough data to create patient twins (19:39)

Connect with Dr. Ulrike Attenberger:

• LinkedIn

Connect with Tobias Heimann:

• LinkedIn

Connect with Siobhan Graham:

• LinkedIn
• Noona App

Connect with Nikki Akar:

• Twitter

Connect with Peter Schardt:

• LinkedIn

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Understanding neurodegenerative diseases require a lot of data to be collected and analyzed by the researchers involved. If they have accurate data that they can get deeper into, they increase the likelihood of identifying patterns that can lead to meaningful conclusions. That is why many researchers are now using AI in analyzing the vast amount of available data. This helps in getting accurate insights faster and sets the stage for future research dimensions.

Today, Lance Ladic, Siemens Healthineers’ Director of Strategic Innovation is joined by three guests - Claire Mackay, Professor of Imaging Neuroscience, University of Oxford, Dr. Andy Saykin, Professor of Radiology and Imaging Sciences at Indiana University School of Medicine in Indianapolis, and Duygu Tosun-Turgut, Associate Professor at the University of California San Francisco in the Department of Radiology and Biomedical Imaging. They’ll help understand the role AI is playing in the fight against neurodegenerative diseases.

Stay tuned to learn about why it is difficult to predict how a neurodegenerative disease will progress in one patient based on data collected from other patients. You’ll also learn about the importance of data sharing among the groups doing research. Lastly, you’ll hear about the role that AI is playing in creating models that can be used in the detection, treatment, and monitoring of patients.

What You’ll Learn in This Episode:

• The relationship between comorbidity and heterogeneity while studying neurodegenerative diseases in patients (01:55)
• How an AI-enabled tool is being used in detecting multiple sclerosis (04:10)
• The future of AI in the treatment of neurodegenerative diseases (06:54)
• The importance of data sharing in the fight against neurodegenerative diseases (12:13)
• How researchers are deploying AI in data collection (15:33)

Connect with Andrew Saykin:

• LinkedIn

Connect with Clare Mackay:

• LinkedIn

Connect with Duygu Tosun:

• LinkedIn

Connect with Lance Ladic:

• LinkedIn

Further reading (as mentioned in this episode):

• “To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)”

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Dementia caused by Alzheimer's is one of the most common symptoms for patients suffering from neurodegenerative diseases. That is why a lot of research has been directed towards understanding the actual impact of the disease on the brain. While there are several studies being conducted, the use of Biomarkers is among those that are in advanced stages. Biomarkers help in understanding as well as monitoring the disease progression in a patient over time.

Today, Lance Ladic, Siemens Healthineers’ Director of Strategic Innovation is joined by three guests - Tammie Benzinger, Professor of Radiology at the Mallinckrodt Institute in St. Louis at Washington University, Dr. Anne Börjesson-Hanson, Director of Clinical Trials at the Department for Aging at Kalinsky University Hospital, and Duygu Tosun-Turgut, Associate Professor at the University of California San Francisco in the Department of Radiology and Biomedical Imaging. They’ll help us understand the progress that has been made so far in the study of Alzheimer’s disease.

Stay tuned to learn about the options that are currently available in the detection of Alzheimer's disease. You’ll also learn about the use of Biomarkers in monitoring the disease’s progression and how that information can be used in managing it. Lastly, you’ll hear about the ongoing Alzheimer's research and the questions they are seeking answers for.

What You’ll Learn in This Episode:

• The process of detecting the Alzheimer's disease (01:43)
• Why it is important to detect the disease early (04:17)
• The role that biomarkers can play in managing the disease (12:35)
• How smartphone and wearable devices are helping in studying the disease (16:32)
• What the future of monitoring this disease will look like (19:20)

Connect with Duygu Tosun:

• LinkedIn

Connect with Dr. Anne Börjesson-Hanson:

• LinkedIn

Connect with Tammie Benzinger:

• LinkedIn

Connect with Lance Ladic:

• LinkedIn

Hosted on Acast. See acast.com/privacy for more information.

The number of people affected by neurodegenerative diseases is expected to rise significantly in the coming years. This is due to a combination of factors such as an increase in the aging population, and lifestyle choices, among others. The task of detecting, treating, and studying brain health issues has brought together the fields of science and technology. They are working to see how tech devices such as wearables, mobile phones, and apps can be used in the fight against these diseases.

Today, Lance Ladic, Siemens Healthineers’ Director of Strategic Innovation is joined by three guests - Claire Mackay, Professor of Imaging Neuroscience, University of Oxford; Dr. Nicolò Zarotti, Trainee Clinical Psychologist at Cambridgeshire and Peterborough NHS Foundation Trust (CPFT) and former Research Associate at Lancaster University; and Dr. Andy Saykin, Professor of Radiology and Imaging Sciences at Indiana University School of Medicine in Indianapolis. They’ll help understand how technology is being used to slow the rise of neurodegenerative diseases.

Stay tuned to learn about the current methods of detecting neurodegenerative diseases and what they involve. You’ll also hear about the important role that wearable devices are playing in the field of brain health. Additionally, you’ll learn about what the medical profession is doing to prepare for an increase in the number of patients.

Note: Nicolò Zarotti's responses and interventions in this episode are based on his work as a researcher and are not intended as clinical advice.

What You’ll Learn in This Episode:

• Factors that influence people’s ability to preserve brain health (02:33)
• The different types of technology that are available for detecting brain health issues (04:08)
• How wearable technologies can help in detecting cognitive decline (06:22)
• How social and global changes impact neurodegenerative diseases detection and treatment (08:19)
• Non-lifestyle factors increasing the risk of neurodegenerative diseases (15:49)
• How the medical profession can be prepared to handle increased brain health cases (17:54)

Connect with Andrew Saykin:

• LinkedIn

Connect with Clare Mackay:

• LinkedIn

Connect with Nicolò Zarotti:

• LinkedIn

Connect with Lance Ladic:

• LinkedIn

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Magnetic Resonance Imaging (MRI) has played a big role in shaping the healthcare industry’s approach to detection, monitoring, and tracking of medical conditions. Since its invention in the 1970s, a lot of improvements have been made to increase its performance and accessibility. As the industry continues to digitalize, we can expect more changes that will make MRIs more powerful and efficient.

The fifty-year history of MRI takes us from early plans scribbled on a napkin at a Pittsburgh Eat n’ Park in 1971, through developments to increase patient comfort and image quality in the 1980s, and into a future where AI can predict, survey, and manage a patient’s likelihood for dementia. In this special episode, Arthur Kaindl talks to experts in the field, Bruce Rosen, M.D., Franz Schmitt, Jürgen Hennig, and Vivek Muthurangu, M.D., about the recent and eventful history of MRI.

In today’s conversation, you’ll hear about the development of the first MRI machine and the challenges faced by the inventors. You’ll learn about the improvements that have been made and the impact that they’ve had on the healthcare industry, as well as what the future holds for MRI machines and what it’ll take to get there.

Some Questions Asked:

• Did you think that MRI was going to be a big innovation? (02:52)
• How did it feel seeing the results of a scan during the development stages? (06:56)
• What exactly can current MRI scanners help us see now that we couldn't see with earlier scanners? (13:11)
• When did you really start to see that certain problems in MRI needed fixing? (18:42)
• What role would you give to artificial intelligence in the future of MRI? (22:07)
• What have you found MRI clinicians need to do, especially when working with pediatric patients? (27:09)

What You’ll Learn in This Episode:

• How the first MRI scanner worked (04:28)
• The purpose of different MRI sounds (09:31)
• The role of MRI in the detection and tracking of neurodegenerative diseases (14:22)
• The impact of imaging technology on how we think about law and criminal behavior (17:13)
• How to make MRI scanners more accessible (24:30)
• The future of MRI scanners (28:46)

Connect with Dr. Bruce Rosen:

• LinkedIn

Connect with Franz Schmitt:

• LinkedIn

Connect with Jürgen Hennig:

• University Medical Center Freiburg

Connect with Vivek Muthurangu:

• Centre for Translational Cardiovascular Imaging

Connect with Arthur Kaindl:

• LinkedIn

Hosted on Acast. See acast.com/privacy for more information.

The invention of computed tomography (CT) was a very important milestone that improved diagnostic capabilities in the field of medicine. Though it was already seen as a game-changer and many improvements were made over the years, it was still limited by image quality and radiation dose, among other things, and not all patients could be diagnosed. This necessitated the development of the photon-counting CT that provides additional clinical information at a low radiation dose and for an increasing number of patients.

In this episode, André Hartung, President of Diagnostic Imaging at Siemens Healthineers, is joined by 4 guests: Anders Persson, Professor of Medical Image Science, Senior Radiologist, and Head of the Centre for Medical Image Science and Visualisation at Linköping University in Sweden; Fabian Bamberg, the Chair of the Department of Radiology at the University of Freiburg in Germany; Thomas Flohr, Head of CT Physics at Siemens Healthineers; and Christian Schröter from the Siemens Healthineers’ Crystal Centre.

Listen to this episode to learn about the limitations of traditional CT scanners and how modern scanners have overcome them. You’ll also hear about photon-counting and the impact it has on image quality. Additionally, you’ll find out the role that artificial intelligence  can play in sorting and analyzing the large amount of data that modern CT scanners produce.

Some Questions Asked:

●     What were the limitations of CT scanning? (01:10)

●     How do you see photon-counting impacting the radiation dose levels being applied? (09:13)

●     Why did the development of photon-counting scanners take so long? (13:17)

●     What is your view on photon-counting CT in the context of biomarker research? (22:05)

What You’ll Learn in This Episode:

●     How current CT scanners work (04:11)

●     How modern CT scanners solved the problem of blooming artifacts (07:43)

●     The benefits offered by modern CT scanners (09:58)

●     The importance of sharing ideas with external partners and institutions (24:41)

Connect with Fabian Bamberg:

●     LinkedIn

Connect with Thomas Flohr:

●     LinkedIn

Connect with André Hartung:

●     LinkedIn

Connect with Anders Persson:

●     LinkedIn

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