In this interview, we talk to Takayuki Baba from Fujitsu Research about ongoing research using artificial intelligence to achieve earlier diagnosis of pancreatic cancer. I am Takayuki Baba, and I am researching medical image diagnosis support technology as a "converging technology" that combines image analysis technology and medical science at Fujitsu Research. Converging technologies combine two or more different social sciences and technology areas to achieve a specific goal and represent a major focus of Fujitsu's R & D. Fujitsu Research has a track record in the research and development of technologies for the detection of multiple types of lesions on computed tomography (CT) images with AI and the retrieval of past CT images with a similar distribution of lesions, which are used in medical diagnostic imaging support technologies to help physicians make diagnoses. Fujitsu and the Southern Tohoku General Hospital have started joint research with Fujitsu Japan Limited and FCOM CORPORATION on AI technology for detecting pancreatic cancer from non-contrast CT images through FCOM, which has been supporting the medical system of Southern Tohoku General Hospital. The survival rate for pancreatic cancer is said to be low, as it is often found when it has already progressed to a state that is difficult to treat.
As artificial intelligence and machine learning technologies continue to be developed, they may become powerful tools in many fields, including that of medicine. AI, complementing human experience and judgement, has already shown promise as a prognostic tool. Recent research using an AI program to help identify, from the results of chest scans, the risk of lung cancer is an example of the technique in action. Lung cancer is the second most common form of cancer worldwide, according to the World Cancer Research Fund. In Australia, it is the leading cause of cancer deaths and Cancer Australia estimates lung cancer accounted for 17.7% of all deaths from cancer in 2021.
"I told my psychiatrist that everyone hates me. He said I was being ridiculous -- everyone hasn't met me yet." Might we incorporate artificial intelligence (AI)-powered software into mental health management? We now have artificial intelligence apps that analyze the human voice and text. I find the prospect of machines invading this therapeutic space both exciting and dangerous.
Which innovations will have the greatest impact in radiotherapy by 2030? That was the question posed in the closing session of last week's ESTRO 2022 congress; and five experts stepped up to respond. As often seen in debate-style ESTRO sessions, competition was intense and gimmicks were plentiful, with all talk titles based on movies and a definite sci-fi twist. Before battle commenced, the audience voted for their preferred innovation based on the presentation titles. This opening vote put personalized inter-fraction adaptation as the winner.
Artificial intelligence, in the form of queried databases, is helping to tackle certain cancers. Algorithms have been developed to cross-reference a patient's medical records, habits and genetic information to spot any early signs of cancer. With cancer, the key problem is about the late diagnosis of cancer and the argument of using artificial intelligence is to identify those members of the population who are at greatest risk and to then bring them in earlier for screening. A pilot has been developed in the U.K., where the focus is particularly with prostate, lung and bowel cancer, and then you can undertake procedures like surgery or administer treatment sooner in order to increase survival rates. Similarly, a U.S. study, published in 2022, found that a machine learning algorithm trained to predict cancer outcomes zeroed in to finds the prostate on male patients and successfully outlines any cancer-suspicious areas without any human supervision.
Building your organization's digital acumen is a critical priority for CIOs today. You won't be future-ready by simply hiring people with the most cutting-edge skills – you also need to create a culture where everyone is continually working on growing and evolving their skills together. I often tell my IT organization that the half-life of an IT professional is about 18 months because technology is constantly changing; thus, it is critical we are all continual learners. That's true not only at Johnson & Johnson; I've observed this across other companies and industries too. To support building our IT organization's digital acumen, we implemented a program that uses artificial intelligence to assess our skills.
Special report AI can study chemical molecules in ways scientists can't comprehend, automatically predicting complex protein structures and designing new drugs, despite having no real understanding of science. The power to design new drugs at scale is no longer limited to Big Pharma. Startups armed with the right algorithms, data, and compute can invent tens of thousands of molecules in just a few hours. New machine learning architectures, including transformers, are automating parts of the design process, helping scientists develop new drugs for difficult diseases like Alzheimer's, cancer, or rare genetic conditions. In 2017, researchers at Google came up with a method to build increasingly bigger and more powerful neural networks.
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain.
New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.