AI News, Artificial intelligence quickly and accurately diagnoses eye diseases and pneumonia

Artificial intelligence quickly and accurately diagnoses eye diseases and pneumonia

'Artificial intelligence (AI) has huge potential to revolutionize disease diagnosis and management by doing analyses and classifications involving immense amounts of data that are difficult for human experts -- and doing them rapidly,' said senior author Kang Zhang, MD, PhD, professor of ophthalmology at Shiley Eye Institute and founding director of the Institute for Genomic Medicine at UC San Diego School of Medicine.

In their new paper, Zhang and colleagues used an AI-based convolutional neural network to review more than 200,000 eye scans conducted with optical coherence tomography, a noninvasive technology that bounces light off the retina to create two- and three-dimensional representations of tissue.

For example, an AI neural network optimized to recognize the discrete anatomical structures of the eye, such as the retina, cornea or optic nerve, can more quickly and efficiently identify and evaluate them when examining images of a whole eye.

With simple training, the authors noted, the machine performed similar to a well-trained ophthalmologist, and could generate a decision on whether or not the patient should be referred for treatment within 30 seconds, with more than 95 percent accuracy.

Such speed and accuracy would represent a profound step forward in medical diagnoses and treatment, according to Zhang, noting that current health care is often lengthy as patients are referred from general physicians to specialists, consuming time and resources and delaying effective treatment.

Artificial Intelligence Quickly and Accurately Diagnoses Eye Diseases and Pneumonia

​Using artificial intelligence and machine learning techniques, researchers at Shiley Eye Institute at UC San Diego Health and University of California San Diego School of Medicine, with colleagues in China, Germany and Texas, have developed a new computational tool to screen patients with common but blinding retinal diseases, potentially speeding diagnoses and treatment.

“Artificial intelligence (AI) has huge potential to revolutionize disease diagnosis and management by doing analyses and classifications involving immense amounts of data that are difficult for human experts — and doing them rapidly,” said senior author Kang Zhang, MD, PhD, professor of ophthalmology at Shiley Eye Institute and founding director of the Institute for Genomic Medicine at UC San Diego School of Medicine.

In their new paper, Zhang and colleagues used an AI-based convolutional neural network to review more than 200,000 eye scans conducted with optical coherence tomography, a noninvasive technology that bounces light off the retina to create two- and three-dimensional representations of tissue.

For example, an AI neural network optimized to recognize the discrete anatomical structures of the eye, such as the retina, cornea or optic nerve, can more quickly and efficiently identify and evaluate them when examining images of a whole eye.

With simple training, the authors noted, the machine performed similar to a well-trained ophthalmologist, and could generate a decision on whether or not the patient should be referred for treatment within 30 seconds, with more than 95 percent accuracy.

Such speed and accuracy would represent a profound step forward in medical diagnoses and treatment, according to Zhang, noting that current health care is often lengthy as patients are referred from general physicians to specialists, consuming time and resources and delaying effective treatment.

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Figure 5 Occlusion Maps and Longitudinal Follow-up OCT Images Comparing Retinal Structural Changes before and after Anti-VEGF Therapy (A) Occlusion maps highlighting areas of pathology in diabetic macular edema (left), choroidal neovascularization (middle), and drusen (right).

(B and C) Horizontal cross-section OCT images through the fovea of patients with wet AMD (B) or diabetic retinopathy with macular edema (C) before and after three monthly intravitreal injections of bevacizumab.

Serious eye diseases accurately diagnosed through artificial intelligence

UC San Diego researchers have developed an artificial intelligence system to quickly and accurately screen patients with potentially blinding retinal diseases to see if urgent treatment is needed.

The system used machine learning to identify macular degeneration and diabetic macular edema within 30 seconds with more than 95 percent accuracy, said Kang Zhang, MD, a professor of ophthalmology at UCSD’s Shiley Eye Institute.

Bacterial pneumonia is treatable with antibiotics, while care for viral pneumonia consists of treating symptoms while the body fights off the infection.

The AI system made retinal disease diagnoses based on data from hundreds of thousands of images and disease data from patients.

And the system also included in its diagnoses the pertinent section of an image, giving doctors an understanding of how they were made.

“The goal is that you take a picture, upload it to the (Internet) cloud, and within 10 seconds we’ll give you a diagnosis, anywhere in the world,” Zhang said.

Artificial intelligence can diagnose and triage retinal diseases

While we might trust virtual assistants to give us directions or recommend as spot for lunch, trusting artificial intelligence (AI) with something as important as a medical diagnosis is a step that many people are not yet willing to take.

'Macular degeneration and diabetic macular edema are the two most common causes of irreversible blindness but are both very treatable if they are caught early,' says senior author Kang Zhang, a professor of ophthalmology at the University of California, San Diego's Shiley Eye Institute.

Earlier studies have used machine learning to study retinal images, but the authors of the new study say their platform goes a step further by using a technique called transfer learning.

This is a type of machine learning in which general knowledge related to classification can be transferred from one disease area to another and can enable the AI system to learn effectively with a much smaller dataset than traditional methods.

He explains that diagnosing and treating retinal diseases normally involves visiting a general medical doctor or an optometrist, then a general ophthalmologist, and finally a retina specialist.

'In addition to economic benefit, there are significant non-economic benefits in increased personal and society productivity regarding a patient's wait time spent to see a doctor and better access to care in remote areas,' he says.

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 Cell (@CellCellPress), the flagship journal of Cell Press, is a bimonthly journal that publishes findings of unusual significance in any area of experimental biology, including but not limited to cell biology, molecular biology, neuroscience, immunology, virology and microbiology, cancer, human genetics, systems biology, signaling, and disease mechanisms and therapeutics.

Convocation Wednesday, May 24, 2018, 2:30 p.m.

McMaster University convocation morning ceremony for the Faculty of Health Sciences (excluding Nursing). Honorary degree recipient: Anne-Marie Zajdlik.