According to a researcher the AI is essentially 4D since it's looking at two CT scans at a time compared to a human who can look at only one.
Deep
learning -- a form of AI -- was able to detect malignant lung nodules
on low-dose chest computed tomography (LDCT) scans with a performance
meeting or exceeding that of expert radiologists, researchers said.
The
system, described in the journal Nature Medicine, provides an
automated image evaluation system to enhance the accuracy of early
lung
cancer diagnosis that could lead to earlier treatment.
The
deep-learning system was compared against radiologists on LDCTs for
patients, some of whom had biopsy confirmed cancer within a year. In
most comparisons, the model performed at or better than radiologists.
Deep
learning is a technique that teaches computers to learn by example.
The
deep-learning system also produced fewer false positives and fewer
false negatives, which could lead to fewer unnecessary follow-up
procedures and fewer missed tumours, if it were used in a clinical
setting.
"Radiologists
generally examine hundreds of two-dimensional images or 'slices' in a
single CT scan but this new machine learning system views the lungs
in a huge, single three-dimensional image," said Mozziyar
Etemadi, 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 require an
enormous computer system of Google-scale.
The concept is novel but the actual engineering of it is also novel
because of the scale," he said.
This
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 screening, said Shravya Shetty, technical
lead at Google.
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