Up@dawn 2.0

Wednesday, March 29, 2017


What happens when diagnosis is automated?
By Siddhartha Mukherjee

One evening last November, a fifty-four-year-old woman from the Bronx arrived at the emergency room at Columbia University’s medical center with a grinding headache. Her vision had become blurry, she told the E.R. doctors, and her left hand felt numb and weak. The doctors examined her and ordered a CT scan of her head.

A few months later, on a morning this January, a team of four radiologists-in-training huddled in front of a computer in a third-floor room of the hospital. The room was windowless and dark, aside from the light from the screen, which looked as if it had been filtered through seawater. The residents filled a cubicle, and Angela Lignelli-Dipple, the chief of neuroradiology at Columbia, stood behind them with a pencil and pad. She was training them to read CT scans.

“It’s easy to diagnose a stroke once the brain is dead and gray,” she said. “The trick is to diagnose the stroke before too many nerve cells begin to die.” Strokes are usually caused by blockages or bleeds, and a neuroradiologist has about a forty-five-minute window to make a diagnosis, so that doctors might be able to intervene—to dissolve a growing clot, say. “Imagine you are in the E.R.,” Lignelli-Dipple continued, raising the ante. “Every minute that passes, some part of the brain is dying. Time lost is brain lost.”

She glanced at a clock on the wall, as the seconds ticked by. “So where’s the problem?” she asked.

Strokes are typically asymmetrical. The blood supply to the brain branches left and right and then breaks into rivulets and tributaries on each side. A clot or a bleed usually affects only one of these branches, leading to a one-sided deficit in a part of the brain. As the nerve cells lose their blood supply and die, the tissue swells subtly. On a scan, the crisp borders between the anatomical structures can turn hazy. Eventually, the tissue shrinks, trailing a parched shadow. But that shadow usually appears on the scan several hours, or even days, after the stroke, when the window of intervention has long closed. “Before that,” Lignelli-Dipple told me, “there’s just a hint of something on a scan”—the premonition of a stroke.

The images on the Bronx woman’s scan cut through the skull from its base to the apex in horizontal planes, like a melon sliced from bottom to top. The residents raced through the layers of images, as if thumbing through a flipbook, calling out the names of the anatomical structures: cerebellum, hippocampus, insular cortex, striatum, corpus callosum, ventricles. Then one of the residents, a man in his late twenties, stopped at a picture and motioned with the tip of a pencil at an area on the right edge of the brain. “There’s something patchy here,” he said. “The borders look hazy.” To me, the whole image looked patchy and hazy—a blur of pixels—but he had obviously seen something unusual.

“Hazy?” Lignelli-Dipple prodded. “Can you describe it a little more?”

The resident fumbled for words. He paused, as if going through the anatomical structures in his mind, weighing the possibilities. “It’s just not uniform.” He shrugged. “I don’t know. Just looks funny.”

Lignelli-Dipple pulled up a second CT scan, taken twenty hours later. The area pinpointed by the resident, about the diameter of a grape, was dull and swollen. A series of further scans, taken days apart, told the rest of the story. A distinct wedge-shaped field of gray appeared. Soon after the woman got to the E.R., neurologists had tried to open the clogged artery with clot-busting drugs, but she had arrived too late. A few hours after the initial scan, she lost consciousness, and was taken to the I.C.U. Two months later, the woman was still in a ward upstairs. The left side of her body—from the upper arms to the leg—was paralyzed.

I walked with Lignelli-Dipple to her office. I was there to learn about learning: How do doctors learn to diagnose? And could machines learn to do it, too?

My own induction into diagnosis began in the fall of 1997, in Boston, as I started my clinical rotations. To prepare, I read a textbook, a classic in medical education, that divided the act of diagnosis into four tidy phases. First, the doctor uses a patient’s history and a physical exam to collect facts about her complaint or condition. Next, this information is collated to generate a comprehensive list of potential causes. Then questions and preliminary tests help eliminate one hypothesis and strengthen another—so-called “differential diagnosis.” Weight is given to how common a disease might be, and to a patient’s prior history, risks, exposures. (“When you hear hoofbeats,” the saying goes, “think horses, not zebras.”) The list narrows; the doctor refines her assessment. In the final phase, definitive lab tests, X-rays, or CT scans are deployed to confirm the hypothesis and seal the diagnosis. Variations of this stepwise process were faithfully reproduced in medical textbooks for decades, and the image of the diagnostician who plods methodically from symptom to cause had been imprinted on generations of medical students.

But the real art of diagnosis, I soon learned, wasn’t so straightforward. My preceptor in medical school was an elegant New Englander with polished loafers and a starched accent. He prided himself on being an expert diagnostician. He would ask a patient to demonstrate the symptom—a cough, say—and then lean back in his chair, letting adjectives roll over his tongue. “Raspy and tinny,” he might say, or “base, with an ejaculated thrum,” as if he were describing a vintage bottle of Bordeaux. To me, all the coughs sounded exactly the same, but I’d play along—“Raspy, yes”—like an anxious impostor at a wine tasting.

The taxonomist of coughs would immediately narrow down the diagnostic possibilities. “It sounds like a pneumonia,” he might say, or “the wet rales of congestive heart failure.” He would then let loose a volley of questions. Had the patient experienced recent weight gain? Was there a history of asbestos exposure? He’d ask the patient to cough again and he’d lean down, listening intently with his stethoscope. Depending on the answers, he might generate another series of possibilities, as if strengthening and weakening synapses. Then, with the élan of a roadside magician, he’d proclaim his diagnosis—“Heart failure!”—and order tests to prove that it was correct. It usually was.

A few years ago, researchers in Brazil studied the brains of expert radiologists in order to understand how they reached their diagnoses. Were these seasoned diagnosticians applying a mental “rule book” to the images, or did they apply “pattern recognition or non-analytical reasoning”?

Twenty-five such radiologists were asked to evaluate X-rays of the lung while inside MRI machines that could track the activities of their brains. (There’s a marvellous series of recursions here: to diagnose diagnosis, the imagers had to be imaged.) X-rays were flashed before them. Some contained a single pathological lesion that might be commonly encountered—perhaps a palm-shaped shadow of a pneumonia, or the dull, opaque wall of fluid that had accumulated behind the lining of the lung. Embedded in a second group of diagnostic images were line drawings of animals; within a third group, the outlines of letters of the alphabet. The radiologists were shown the three types of images in random order, and then asked to call out the name of the lesion, the animal, or the letter as quickly as possible while the MRI machine traced the activity of their brains. It took the radiologists an average of 1.33 seconds to come up with a diagnosis. In all three cases, the same areas of the brain lit up: a wide delta of neurons near the left ear, and a moth-shaped band above the posterior base of the skull.

“Our results support the hypothesis that a process similar to naming things in everyday life occurs when a physician promptly recognizes a characteristic and previously known lesion,” the researchers concluded. Identifying a lesion was a process similar to naming the animal. When you recognize a rhinoceros, you’re not considering and eliminating alternative candidates. Nor are you mentally fusing a unicorn, an armadillo, and a small elephant. You recognize a rhinoceros in its totality—as a pattern. The same was true for radiologists. They weren’t cogitating, recollecting, differentiating; they were seeing a commonplace object. For my preceptor, similarly, those wet rales were as recognizable as a familiar jingle.

In 1945, the British philosopher Gilbert Ryle gave an influential lecture about two kinds of knowledge. A child knows that a bicycle has two wheels, that its tires are filled with air, and that you ride the contraption by pushing its pedals forward in circles. Ryle termed this kind of knowledge—the factual, propositional kind—“knowing that.” But to learn to ride a bicycle involves another realm of learning. A child learns how to ride by falling off, by balancing herself on two wheels, by going over potholes. Ryle termed this kind of knowledge—implicit, experiential, skill-based—“knowing how.”

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