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This wiki is intended to be used as a reference for the concepts and instructions of the project. If you are new for the project and Framework, it is important that you follow this wiki with attention. Although it is an important piece of information documentation, this wiki is not supposed to replace the official documentation of each tool, prototype or used library.
MIMBCD-UI project deals with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). These deep networks will incorporate information from several modes: magnetic resonance imaging volumes (MRI), ultrasound images, mammographic images (both views CC and MLO) and text. The proposed algorithm, called for multimodality CNNs (MMCNNs) will have the ability to process multimodal information at a unified and sustained manner. This methodology needs to "learn" what are the masses and calcifications. So that is necessary to collect the ground-truth or notes of the masses and calcifications provided by medical experts. For the collection of these notes, the design and development of an interface is necessary to allow users (in this case, medical specialists) to display various types of image (i.e., ultrasound, MRI and mammography), and that also allows for user interaction, particularly in providing the notes of the masses and calcifications. For these reasons, it is crucial for the development of this project, cooperation with experts providing the above notes.
Medical imaging diagnosis is a routine effort performed by radiologists to help diagnose or monitor a patient's medical condition. Medical imaging diagnosis allows physicians to identify pathologies by decoding tissue characteristics while examining visual properties in medical imaging. It plays a central role in modern medicine, particularly in the prevention and diagnosis of breast cancer, one of the leading causes of mortality worldwide. Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012. However, proper classification, location, detection, targeting and registration of tumors requires the use of different imaging modalities that contribute to diagnostic reliability.
With the hype of new Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) algorithms, automated agents (assisted by AI) are closer to the radiology room than ever before. This proximity becomes highly relevant for the medical community, which is interested in solutions to support clinical diagnosis. Greater stakeholder engagement requires sufficient validation and continuously monitored emerging AI tools as well as available breast cancer screening data. That said, national health systems are increasingly in need of methods, such as our solutions, for ongoing clinical diagnostic support to support medical decision-making.
Our goal is to provide AI solutions for radiology. We bring radiology experts and software engineers together to optimize healthcare professionals workload through the development of high-performance AI tools.
The aim of this project is to develop a system supported by intelligent agents for diagnosing medical images in the field of breast cancer. To address the effects of lack of clarity in AI results, we provide a service where our intelligent agents are applied to the breast cancer domain at different levels of medical expertise and multiple clinical workflows. Despite the promise of assisting clinicians in the decision-making process, there are two initial challenges that our services aim to address: (i) the lack of available and cured medical data to be consumed by AI algorithms; and (ii) the fact that medical professionals often find it difficult to understand how an AI system turns its outcome into a final medical decision. The goal is to develop a disruptive platform with the introduction of various AI techniques using an intelligent agent to communicate with the various physicians in an interpretable way. Specifically, focusing on how the multimodality and assistance of an AI model can add value to the medical workflow.
A significant number of technologies under development and in prototype or clinical trials, suggest that AI-powered diagnostic departments will feature in many future clinical institutions [2]. Diagnostic institutions are leveraging pattern recognition and DL to reduce diagnosis turnabout time, and improve pathology workflow accuracy and efficiency of the diagnostic.
Our solution is being used in more than nine public and private health institutions [1] in Portugal. We are currently scaling our services and solutions outside Portugal, where we already have the collaboration of more than 300 international physicians. From our networking of physicians, many belong to excellent radiology associations and institutions, such as the American College of Radiology (ACR) with collaboration with this project. Within the ACR (among other institutions), we will be able to further validate and promote our solution. Nevertheless, we already have an invention [2] registered as a national patent, with the number 116801 and reference DP/01/2021/74923, as well as, registered internationally, as the number PCT/PT2021/050029.
[1] Francisco Maria Calisto, Carlos Santiago, Nuno Nunes, Jacinto C. Nascimento, Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification, International Journal of Human-Computer Studies, Volume 150, 2021, 102607, ISSN 1071-5819, https://doi.org/10.1016/j.ijhcs.2021.102607
[2] Calisto, F. M. (2020). Breast Cancer Medical Imaging Multimodality Lesion Contours Annotating Method. Instituto Superior Técnico. https://doi.org/10.13140/RG.2.2.14792.55049
[3] Francisco Maria Calisto, Carlos Santiago, Nuno Nunes, Jacinto C. Nascimento, Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification, International Journal of Human-Computer Studies, Volume 150, 2021, 102607, ISSN 1071-5819, https://doi.org/10.1016/j.ijhcs.2021.102607