Cancer and AI based image analysis


After decades of trying to find a drug-based cancer treatment, doctors now hope the technology will help both diagnose and treat cancer. Many methods of detecting and treating cancer, which could help save lives even from the most devastating prospects, emerged thanks to the advancement of artificial intelligence (AI) and medical image analysis fields. Let’s take a closer look at the changes these technologies can bring to the battle against cancer.

Image analysis for rapid and accurate cancer detection

When it comes to aggressive forms of cancer, scientists and researchers are aware of the imperative nature of a rapid diagnosis. Many cancers, such as lung cancer mesothelioma, are often misdiagnosed or detected too late, leaving patients with little or no choice in treatment options and with a short life expectancy With AI in the picture, doctors expect a faster detection of cancer.

Researchers in Germany, France and the United States have already revealed that by detecting cancer, AI technology is more accurate than a doctor’s eye. His studies showed that AI-based imaging software can detect cancer in 95% of images of cancerous moles and benign spots. In comparison, doctors were able to correctly detect cancer in only 87% of the same images.

AI and Collaborative Cancer Cloud Systems

Many research firms that do not have medical associations and therefore access to patient data are forced to train their AI systems in fabricated patient records. This hinders the evolution of AI algorithms that aid in the detection, diagnosis, and treatment of disease, as working with real patient data is crucial to their growth. IBM called the lack of real-world data the main reason its Watson AI provided incorrect treatment recommendations.

He Collaborative cloud against cancer (CCC) can solve this problem, as all cancer patient information can now be added to this secure, encrypted cloud-based system developed by Intel. CCC data is useful for the formation of new AI systems, as well as for the work of already mature AI software. In the latter case, it can reduce the time it takes to detect, diagnose, and plan cancer treatment in just 24 hours. For example, the AI ​​may be asked to compare data from newly diagnosed patients with a large amount of data from older patients in the CCC. Upon completion of the analysis, physicians will obtain accurate information about the type of DNA mutations in a new patient and how these mutations were previously treated.

Analysis of AI-based DNA patterns

The London Cancer Research Institute (ICR) developed a new technique called Repeated Evolution of Cancer, also known as a revolver. According to this method, an AI system chooses a mutated DNA pattern within cancer cells and uses this information to predict further genetic changes. In addition, the ICR research team found a link between certain DNA sequences and treatment outcomes. This suggests that the DNA patterns of cancer-treated patients can be used to give a more accurate prognosis to newly diagnosed patients.

This was stated by Dr. Andrea Sottotiva, who led the ICR study AI is a revolutionary tool to perform fast and accurate image analysis and comparison of DNA patterns. She hopes this tool will help health care providers predict the ever-changing disease and work more effectively with patients before cancer cells become drug-resistant.

Home v3

Home v3 Google is an example of AI-based imaging software that works with DNA and cancer data. Google’s system has an open source algorithm, trained to identify 1,000 different classes of objects. As part of the training, the developers used thousands of images of healthy and cancerous tissues Atlas of the cancer genome. Thanks to a sophisticated subset of machine learning called “deep learning,” the software was able to advance on its own and learn the difference between the two types of tissues and the different types of cancer cells.

Aristotelis Tsirigos, a pathologist at the NYU School of Medicine and lead author of a study on AI and cancer, said that Inception v3 can also find mutations within the DNA of cancer cells, helping doctors discover mutations and advance the disease with proper treatment. . He said: “These mutations that cause cancer appear to have microscopic effects that the algorithm can detect.”

Google’s system is so advanced that it is continually learning within its own “black box,” with no human interference. Neither programmers nor doctors know exactly how to make concrete discoveries. This is something that worries the medical field: after all, machines can make mistakes, and if creators don’t know how algorithms work, they won’t be able to find or prevent calculation errors. Despite this, specialists in Cornell say that with the 99% accuracy of Inception v3, the risk is worth the reward.

The future

Artificial intelligence and imaging can help healthcare professionals offer cancer patients more treatment options. A recent survey proved it 60% of Americans would be open to genetic testing to determine their risk of developing cancer, while 44% of respondents would rely on AI to make a cancer diagnosis and offer treatment recommendations. It is obvious that patients are already open to new approaches.

A report from Signify Research it also states that hospitals are expected to spend $ 2 million annually on AI for medical imaging devices by 2023. In addition, according to Azati Software, an AI medical system can cost between $ 35,000 and $ 100,000 on average. However, while initial equipment costs may be high, an ABI Research study in June 2018 estimates that AI applications can help the global healthcare sector save up to $ 52 billion by 2021.

Medical image analysis by ScienceSoft

Innovates in the prevention, diagnosis and treatment of diseases with an efficient analysis of medical images.

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