How has technology changed cancer research?
Every year, 19 October is International Breast Cancer Day, a day to raise awareness about prevention and to remember those who have battled the disease. Also, every February 4th, World Cancer Day is celebrated with the aim of promoting and supporting cancer research in order to slow down the progression of the disease and increase survival rates and the quality of life of patients.
In recent years, advances in technology have allowed the implementation of great improvements in early detection, thanks to tools such as Artificial Intelligence, Deep Learning, or Augmented Reality, gaining reliability and saving time and money.
Plain Concepts has developed technological solutions aimed to be applied to pioneering diagnostic techniques such as organ segmentation in medical images with AI, AR medical assistants, or BI-RADS classifiers, a method that enables the classification of mammographic findings. If you are not yet familiar with these technologies, here is a simple explanation.
Cancer Technology advancements
AI in cancer diagnosis
This technology has already proven effective in improving the diagnosis and treatment of serious diseases such as cancer.
Numerous treatments are already underway that aim to optimise breast cancer detection and therapies. For example, a risk assessment model that collates thousands of mammograms can be used to predict the risk of developing breast cancer up to five years before the first symptoms are recorded.
There is also ‘Dream Challenges’, promoted by the UPV, the UV and the CSIC and aimed at aiding diagnosis and reducing false positives. Digital mammograms are complemented with AI tools, with results close to 90% and capable of reducing the number of suspicious areas or false alarms through tools such as neural networks or predictive algorithms.
Automated BI-RADS Classifier
We have developed a tool for the diagnosis of breast cancer through the use of Deep Learning techniques, using models based on Deep Neural Networks that enable an automated classification of a patient’s mammograms, which makes it easier to suggest recommendations for further testing in case of anomalies.
Segmentation of organs in medical images with artificial intelligence
Over the last few years, automating the analysis of medical images used in the diagnostic-therapeutic cycle has become a priority in modern medicine. Therefore, the possibility of making a diagnosis using images has become invaluable. The use of Artificial Intelligence techniques, more specifically semantic segmentation, can be very useful in the detection of organs for medical imaging. The use of Deep Learning techniques can help specialists during their diagnostic process and decision-making.
One of the advantages of using this technique is the integration of the model in any of the hardware devices used by specialists to visualize or represent medical images (mobile devices, HoloLens, or computers), which make it possible to show there the organs are located at all times and for example be able to plan an optimal puncture point in biopsies, producing the least possible damage to adjacent organs.
The Other Reality in Biopsies: HoloLens for Medicine
Plain Concepts has developed an AR-based medical assistant that, through the use of the HoloLens, the holographic glasses developed by Microsoft, guides the physician and provides the complete patient history during a biopsy. This application makes it possible through a QR code to connect to the hospital’s communication and image archiving system and receive all the necessary patient information, which would be reproduced in 3D by the HoloLens before performing the biopsy.
The operation is very simple: six metal cores are placed on the patient’s body. When the doctor wears the HoloLens, the 3D images appear in front of the doctor, and then it’s possible to make “cuts,” indicating exactly where to perform the puncture. This makes it possible to mark a line with the trajectory that the needle should follow, as well as the inclination and depth that the needle should reach. This technology will save costs in medicine while facilitating faster and more accurate diagnoses.
If you want to know how generative AI is changing the healthcare sector, don’t miss our article “Generative AI in healthcare: The next big revolution in medical care and research,” where we analyze some of the most important use cases in various healthcare segments and the implications and possibilities for the future. In addition, if you want to start implementing this technology in your company in a safe and successful way, we present our exclusive OpenAI Framework, with which we will help you find the use cases that best fit your needs, implement them in your organization and protect your data.
