Scientist at Google has developed an artificial intelligence model which they claim is better at diagnosing lung cancer than human experts, an advance which can procure a deadly disease at an earlier stage.
Deep Learning: It is a form of Artificial Intelligence, which is able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans with a performance meeting or exceeding that of expert radiologists, as said by the researchers.
The system is described in the journal Nature Medicine, which provides an evaluation system for the automated image for enhancing the accuracy of the early lung cancer diagnosis that could lead to earlier treatment.
The system of Deep-learning was compared against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most of the companies, the model performed at or better than radiologists.
Deep learning is a technique that teaches computers to learn by example.
Hundred of images which two-dimensional are examined by radiologists in a single CT scan, but in this new machine learning system lungs are viewed in a three- dimensional image, as said by Mozziyar Etemadi, who is a research assistant professor at Northwestern University in the US.
AI in 3D can be much more sensitive in its ability to detect early lung cancer than the human eye looking at 2D images. This is technically 4D because it is not only looking at one CT scan but two over time, Etemadi said.
"In order to build the AI to view the CTs in this way, you acquire an enormous computer system of Google-scale. the concept is novel but the actual engineering of it is also novel because of the scale."
As per the words of Shetty, Our work examines ways AI can be used to improve the accuracy and optimize the screening programs. The results are promising, and we look forward to continuing our work with partners and peers.
Large clinical trials across the US and Europe have shown the chest screening can easily identify cancer and reduce death rates, as noticed by the researchers.
The deep learning system utilizes both the primary CT scan and whenever available, a prior CT scan from the patient as input.
Prior CT scans are very useful for predicting the lungs cancer malignancy risk because the growth rate of suspicious lung nodules can be indicative of malignancy.
The computer was trained using fully de-identified, biopsy-confirmed low-dose chest CT scans.
Google scientists developed the deep-learning model and applied it to 6716 de-identified CT scan sets to validate the accuracy of its new system.
The AI-powered system was founded and was able to spot sometimes-minuscule malignant lung nodules with a model of 0.94 test cases.
The researcher cautioned that these findings need to be clinically validated in large patient populations.
However, they said this model may assist in improving the management and outcome of patients with lung cancer.