A new study has demonstrated the development of an artificial intelligence (AI) tool to assist in real-time diagnosis during surgery. It can boost the quality of images to increase the accuracy of rapid diagnostics.
The research conducted at the Brigham and Women’s Hospital in Boston, Massachusetts, has developed a deep learning algorithm which can teach itself how to identify similar features in large pathology image repositories to help diagnose and generate treatment guides for rare diseases. The study was published in Nature Biomedical Engineering.
"We are using the power of artificial intelligence to address an age-old problem at the intersection of surgery and pathology," said the corresponding author of the study Faisal Mahmood, PhD, of the Division of Computational Pathology at US-based Brigham and Women's Hospital.
Touted Self-Supervised Image Search for Histology (SISH), the researchers developed a deep-learning model that can be used to translate between frozen sections and more commonly used formalin-fixed and paraffin-embedded (FFPE) tissue samples -- this method preserves tissue in a way that produces high-quality images but is labour-intensive and can take several days. They further noted that this system has the potential to “improve pathology training, disease subtyping, tumour identification, and rare morphology identification.”
At its most basic, deep learning attempts to imitate the complex neural networks in our own brains through algorithms. These algorithms were taught to learn things about data sets by finding patterns and trends, much like we do in our daily experience.
"Our work shows that AI has the potential to make a time-sensitive, critical diagnosis easier and more accessible to pathologists. And it could potentially be applied to any type of cancer surgery. It opens up many possibilities for improving diagnosis and patient care," Mahmood added.
Earlier too, several research reports have focused on usage of AI on medical diagnoses. For example, machine learning researchers at MIT's computer science and artificial intelligence lab (CSAIL), developed an AI diagnostic system in January that helps make a decision or diagnosis based on its digital findings.
"Machine learning systems are now being deployed in settings to [complement] human decision makers," said CSAIL researchers. In the research paper, the researchers said the new study focuses not just on clinical applications of AI, but also on areas such as content moderation with social media sites such as Facebook or YouTube for better decision making.
According to Boston-based research firm BCC Research, the global market for AI medical diagnostics is estimated to increase from $748 million in 2021 to reach $4.0 billion by 2026, at a compound annual growth rate (CAGR) of 39.8% during the forecast period of 2021-2026.