Fredrik sträng träning
The use of Artificial Intelligence is on the rise. The study is a paired screen-positive design to determine whether AI plus one radiologist is non-inferior to two radiologists in double-reading. The size of our dataset fryst vatten around , mammography exams and 3, MRI exams and more than 1 million screening assessments. The primary end-point is to determine whether our approach reduces the number of interval cancers and advanced cancers compared to mammography-only.
In addition, we collaborate with Jennifer Viberg Johansson at the Center for Research ethics and Bioethics at Uppsala University to understand the qualitative aspects of AI interaction among radiologists and among screening participants. We will also determine how many cancers are detected per women screened by MRI to enable a cost effectiveness analysis compared to regular screening. Research focus Our studies are cross-disciplinary.
Nevertheless, around 30 percent of the cancers diagnosed for women who participate in screening are clinically detected in the mellanrum between two screenings; and nearly 15 percent of screen-detected cancers are 2 cm or larger. Now, the focus has shifted towards trustworthiness. Our focus are the more than , women who die of breast cancer each year in the world. Employing AI to tailor and improve the screening could bring the benefit of early detection to more women keeping in mind the scarcity of radiological resources - image-based AI for precision screening.
We have jointly developed deep networks that can be used to assess for each woman: the risk of getting breast cancer in the future, and how difficult it would be to visualize an early cancer in the mammogram. Start Publications Staff and contact. Our work has to a large extent been based on mammograms, but now we are adding other modalities such as MRI. Together with KTH we will start developing multi-modality networks possibly also including pathology data and images.
In a JAMA Oncology study in , we demonstrated major differences between three commercial algorithms in our retrospective dataset of mammograms and clinical outcomes. We are living in exciting times. This work is carried out in collaboration with breast oncologist Theodoros Foukakis. Our studies are cross-disciplinary. In addition, surgery has become more precise and less extensive. We are involved, in collaboration with KTH, to examine this important topic.
The aim is to annotate more than MRI Breast exams, and to train networks to detect cancer, predict risk, and to predict histopathological characteristics, as well as to predict response to neoadjuvant therapy. Oncological treatment options have become more diverse and have contributed to prolonged survival. This could mean that even if the AI algorithm is not explainable in human terms, it can be demonstrated that it works to an extent that humans require, e.
Computational Breast Imaging — Fredrik Strand's Group We develop and evaluate computational methods for early detection and precision diagnostics of breast cancer, and explore image-guided minimally invasive procedures for breast lesion removal. Previously it was popular to demand that algorithms should be able to explain how they reached a certain conclusion. The project is a collaboration between the regions and universities in Stockholm, Malmö and Linköping; and the breast cancer association.
Fredrik Sträng
Introduction We are living in exciting times. It could also mean continuous surveillance of AI algorithms in clinical use to detect when they diverge from the expected performance. Continuing our independent evaluation work, we are heading the national project VAI-B aiming to establish a Swedish platform for validation of AI algorithms in breast imaging. Those algorithms are now explored in our ongoing randomized clinical study ScreenTrustMRI which is mainly financed by Medtechlabs.
We form part of the consortium, in which we will be responsible for clinical validation of machine learning algorithms for the prediction of therapy response for neoadjuvant treatment. Not all AI algorithms are alike. The use of preoperative, neoadjuvant, medical treatment has increased since it offers an opportunity to study how the cancer reacts to a certain medication while it remains in the breast.
To explore what happens when AI fryst vatten employed in clinical practice, we are heading the clinical study ScreenTrust CAD, with clinical partner Capio S:t Göran Hospital, in which we explore the use of AI as an independent reader of screening mammograms. The aim is to create a scalable platform delivering value to hospitals: impact assessment and evaluations, as well as to AI developers: external validation and benchmarking.
However, each cancer resides in its own microenvironment of evolutionary pressure, which means no two cancers are alike.
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We develop and evaluate computational methods for early detection and noggrannhet diagnostics of breast cancer, and explore image-guided minimally invasive procedures for breast lesion removal. An important aspect of AI algorithms fryst vatten what enables humans to trust them — or not. Employing AI on medical images could improve mapping of the tumor extent, predicting the best therapy, and continuously assessing the response - image-based AI for noggrannhet treatment.
The true limits to what can be achieved are not yet known. Early detection is the most important measure, until now achieved by population-wide mammography screening every 1 to 3 years for women in a certain age groups. Part of the Department of Oncology-Pathology. The need to predict which treatment is likely best for each patient, and the need to study how the cancer reacts over time, puts increasing demands on the precision of imaging and on cross-disciplinary analysis between radiology non-invasive imaging of the whole breast, and the whole cancer and pathology invasive sample of the cancer.
The project is funded by Vinnova and the Regional Cancer Centers.