Science | DOI: 10.1145/2063176.2063182
Better Medicine through
Computers that tease out patterns from clinical data
could improve patient diagnosis and care.
MedIcIne can Be as much art as science, a detective story in which doctors rely not only on lab tests and x-rays, but on their own
experience and clues from a patient’s
history to develop diagnoses or predict
future health problems. But all of those
lab tests, blood pressure readings, magnetic resonance imaging (MRI) scans,
electrocardiograms, and billing codes
add up to reams of data, which before
too long will be joined by individual
gene sequencing. Computer scientists
are increasingly applying machine
learning techniques to all that data,
searching for patterns that can aid diagnosis and improve clinical care.
IMage Courtesy of kenjI suzukI
“Machine learning plays, I think, an
essential role in medical image analysis nowadays,” says Kenji Suzuki, assistant professor of radiology and medical
physics at the University of Chicago’s
Comprehensive Cancer Center. Suzuki
has been working on automating the
detection of cancerous lesions in images from x-rays or computed tomography
scans. Considering that radiologists
may miss 12%–30% of lung cancers in
such scans, a machine learning tool offers great potential.
Since the mid-1980s, computer sci-
(a) (b) (c) (d)
Kenji suzuki and colleagues’ comparison of their rib-suppressed temporal-subtraction
(ts) images with conventional ts images: (a) previous chest radiographs, (b) current chest
radiographs of the same patient, (c) rib-suppressed ts images with fewer rib artifacts, and
(d) conventional ts images.