Health & Fitness

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms -- radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming. An engineering team has now developed a new approach which can cut human cost down by 90%, by enabling the automatic acquisition of supervision signals from hundreds of thousands of radiology reports at the same time. It attains a high accuracy in predictions, surpassing its counterpart of conventional medical image diagnosis employing AI algorithms.

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A research team has developed an innovative technology for in vivo imaging of the important biological processes involved in the injury and repair of spinal cords, paving the way for a better understanding of the pathology and potential treatment of spinal cord injury (SCI).

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A new strategy in tissue engineering has been developed: Tiny spherical microscaffolds are created in a high precision 3D printer. They are cultivated with living cells and then assembled. The cells keep multiplying, creating a tissue, the scaffolds are eventually degraded.

from Top Health News -- ScienceDaily https://ift.tt/SyoLAeG

Conduct disorder (CD) is a common yet complex psychiatric disorder featuring aggressive and destructive behavior. Factors contributing to the development of CD span biological, psychological, and social domains. Researchers have identified a myriad of risk factors that could help predict CD, but they are often considered in isolation. Now, a new study uses a machine-learning approach for the first time to assess risk factors across all three domains in combination and predict later development of CD with high accuracy.

from Top Health News -- ScienceDaily https://ift.tt/1bwKEF9

Scientists have discovered a new way to inhibit the most commonly mutated gene underlying human tumor growth, paving the way for new treatments for cancer and a host of other diseases.

from Top Health News -- ScienceDaily https://ift.tt/tNM2X5L