- Machine Learning Could Reduce Lab Testing for ICU Patients
"Radiation dose has been a significant issue for patients undergoing CT scans. Our machine learning technique is superior, or, at the very least, comparable, to the iterative techniques used in this study for enabling low-radiation dose CT," said Ge Wang, a professor of biomedical engineering at Rensselaer and a corresponding author on the paper published by the researchers.
"It's a high-level conclusion that carries a powerful message. It's time for machine learning to rapidly take off and, hopefully, take over," Wang added.
The research team set out to tackle the problem using a machine learning framework. Specifically, they developed a dedicated deep neural network and compared their best results to the best of what three major commercial CT scanners could produce with iterative reconstruction techniques.
Rensselaer Team Works with MGH, Harvard Radiologists
The Rensselaer researchers worked close with Mannudeep Kalra, a professor of radiology at Massachusetts General Hospital (MGH) and Harvard Medical School and a corresponding author on the paper.
The researchers were looking to determine how the performance of their deep learning approach compared to the representative iterative algorithms currently being used clinically.
Several radiologists from MGH and Harvard Medical School assessed all the CT images. The deep learning algorithms developed by the Rensselaer team performed as well as, or better than, those current iterative techniques in an overwhelming majority of cases, Wang said.
Researchers found that their deep learning method is also much quicker and allows the radiologists to fine-tune the images based on clinical requirements.
These positive results were realized without access to the raw data from all the CT scanners. Wang pointed out that if the raw CT data is made available, a more specialized deep learning algorithm should perform even better.
"This has radiologists in the loop," Wang said. "In other words, this means that we can integrate machine intelligence and human intelligence together in the deep learning framework, facilitating clinical translation."
"We are excited to show the community that machine learning methods are potentially better than the traditional methods," Wang said. "It sends the scientific community a strong signal. We should go for machine learning."
Deepak Vashishth, director of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer, added: "Professor Wang's work is an excellent example of how advances in artificial intelligence, and machine and deep learning can improve biomedical tools and practices by addressing hard problems — in this case helping to provide high-quality CT images using a lower radiation dose. Transformative developments from these collaborative teams will lead to more precise and personalized medicine.”
Northwestern Medicine, Google AI System Outperforms Radiologists
A recent study by Northwestern Medicine and Google used artificial intelligence to improve detection of malignant lung cancer on low-dose CT scans.
The AI system provided an automated image evaluation tool to enhance the accuracy of early lung cancer diagnosis, resulting in earlier treatment.
The researchers compared the AI system performance to radiologists on low-dose CT scans of patients, some of whom had biopsy-confirmed cancer within a year. In most cases, the model performed as well as or better than the radiologists.
The AI system uses both the primary CT scan and a prior CT scan from the patient as input. Prior CT scans are useful in predicting lung cancer malignancy risk because the growth rate of suspicious lung nodules can suggest malignancy. The computer was trained using de-identified, biopsy-confirmed low-dose chest CT scans.
“This area of research is incredibly important, as lung cancer has the highest rate of mortality among all cancers, and there are many challenges in the way of broad adoption of lung cancer screening,” said Shravya Shetty, technical lead at Google.
