Medical imaging is an essential tool to support clinical diagnosis and treatment of diseases.
Physicians use different diagnostic methods based on imaging such as X-rays, ultrasound scans, CT scans and MRIs, among others, to detect and identify anomalies and determine disease progression.
We also use complementary diagnostic methods based on images in other medical specialties, such as Digital Pathology (Pathological Anatomy) and Cardiology (Digital Electrocardiogram), Ophthalmology (Eye Fundus).
With the intention of gaining effectiveness in the interpretation of medical images and efficiency in healthcare systems, advances have been achieved through the incorporation of Artificial Intelligence (AI) that helps us to identify patterns based on analysis of a large volume of data in real time, gaining accuracy in the interpretation of studies.
To learn more about more information about the advances in AI applied to medical imaging, I have compiled some data that illustrate the advances in the use of Artificial Intelligence.I have compiled some data that illustrate the advances in the use of Artificial Intelligence, as well as explore industry trends and the future of AI in medical imaging.
Impact of Artificial Intelligence (AI) applied to medical imaging.
Currently available AI algorithms may be capable of analyzing an infinite amount of data and comparing it to identify patterns at a high level of detail and at a higher speed. identify patterns at a high level of detail and at a higher speed than the human eye. than the human eye.
However, achieving technological solutions to support diagnostics with a high level of accuracy is a lengthy process and we are still taking the first steps.
We have to thank the advances in the field of graphics processing units (GPUs), as this has allowed us to training large-scale neural networksusing massive data, generating important milestones, not only in medicine but in many sectors.
AlexNet (2012)
It is a convolutional neural network (CNN) that is 8 layers deep, pre-trained and capable of classifying images into 1,000 object categories.
Its success sparked interest in the
Deep Learning
related to image analysis.
Conflicting Generative Networks (GANs) (2014)
With GANs, the landscape changed completely, opening the way to the generation of synthetic medical images.
In this case, two neural networks are trained to compete with each other, with the intention of generating authentic data from training data.
The confluence of factors has laid the groundwork for the advancement of Deep Learning models.
Deep Learning
with specific applications within the medical context, such as the one created for classification of COVID-19 radiographs and other examples.
Although AI applied to medical imaging in no way replaces professional judgment, it does improve the speed of diagnosis.
U-NET (2015)
This neural network model is pioneering because its architecture has specialization in biomedical image segmentation.
U-NET’s popularity derives from its level of accuracy, even when a limited data set is available.
Advantages of using Artificial Intelligence
AI helps physicians by providing them with a greater number of diagnostic support tools that enable them to make a more accurate diagnosis when interpreting studies.
Increased diagnostic accuracy
AI algorithms are trained to detect patterns, however subtle detect patterns, however subtle they may be.
Si a esto le sumamos la posibilidad de integrar datos clínicos, el médico logrará tener más herramientas para dar con un diagnóstico más certero y personalizado.
Gracias a este nivel de precisión, se podrán ofrecer las opciones de tratamiento más efectivas, de forma oportuna.
Optimizes workflow
For companies and professionals specializing in medical imaging, the existence of AI tools that can interpret images saves time and effort. save time and effort that was previously spent on repetitive or tedious tasks that are part of the daily routine.
In this way, the physician will be able to optimize his or her workflow and gain time to devote to patient care..
Early detection of pathologies
When analyzing large volumes of data, AI can be used for detect early signs of disease on medical images where physicians can direct their attention during the diagnostic interpretation process and validate these findings. With this confirmation, it is possible that they are early detection of some tumors, lesions or congenital anomalies, so that action can be taken in time, improving the prognosis of patients.
What is the future of AI applied to medical imaging?
A promising future is estimated for the AI-driven medical imaging segment.
There are currently several emerging AI-based technologies in the midst of development, including research areas such as:
-
Explainable IA (XAI)
The Explainable AI (XAI) is a type of technology with the ability to explain its decisions and the process that led to that decision in a human-understandable way.
This is useful for the healthcare professional, as it adds transparency and reliability to the diagnosis generated by an AI, since it can understand the reasoning behind the recommendation offered..
-
Federated learning
Refers to a decentralized decentralized approach modelwhere AI models are trained with data belonging to various entities, medical institutions and health centers, all without sharing sensitive data.
The notorious advantage of federated learning is the possibility of feeding the model with data from various clinical settings.
-
Multimodal IA
With Multimodal AI various types of data (images, text, biological signals, etc.) are integrated from different imaging modalities (magnetic resonance imaging, computed tomography, PET) and other sources.
By integrating such a large amount of data, the aim is to obtain improvements in the quality of diagnosis that AI can offer.
In addition to all of the above, it is also expected that a stage of collaboration between various technologies will be enhanced, such as the 3D printing, Augmented Reality (AR), robotics and automation. Intelligent surgical robots are already a reality, but everything points to a total integration with the Internet of Medical Things (Internet of Medical Things or IoMT).
Finally, the potential of Artificial Intelligence in the field of medical imaging is undoubtedly significant.
Nevertheless, it is imperative to recognize and address certain essential prerequisites for the full integration of this technology.
Es crucial asegurar la interoperabilidad de las imágenes médicas a través de diversas especialidades, extendiéndose más allá de la radiología y adoptando formatos estándar, como el DICOM, en lugar de formatos propietarios.
Además, la calidad de los datos utilizados para entrenar los algoritmos de Inteligencia Artificial es de suma importancia.
Esto incluye no solo imágenes, sino también vídeos, audios y datos demográficos específicos de cada caso médico.
Cuanto más enriquecido y completo sea el conjunto de datos de cada situación clínica, mejores serán los resultados obtenidos.
As for the implementation of these projects, apart from the investment in the necessary medical equipment, how these images will be stored and the optimal duration of their storage must be considered in the design.
These decisions lead the project teams (clinical and technical) to face national and European regulations (certifications), as well as ethical dilemmas, for which there is still no clear and defined framework.
This situation underlines the need for meticulous planning and a major consideration of all aspects involved in the adoption of Artificial Intelligence in medicine.
Dra.
Yulisa Dominguez,
Senior
Product Manager
of Digital Health in Grupo Oesía.