Sunday December 15, 2019
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Google Claims Eye Doctors Can Turn More Effective Using AI

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm. 

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The research team at Google AI believes that some of these pitfalls may be avoided if the computer can "explain" its predictions. Pixabay

As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.

In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.

The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.

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To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance. Pixabay

“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.

Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.

According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.

The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.

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Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said. Pixabay

To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.

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Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.

With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)

Next Story

AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

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Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)