On September 22, 2016, researches at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital's Institute of Health Professions created a computer system that could help screen and determine if young children have speech and language disorders and potentially provide information for actual diagnosis. The system would be able to analyze audio recordings of children's performances on a basic storytelling test that is standardized. The kids would be presented with a series of images accompanied by a narrative, they would then be asked to retell the story in their own words.
The benefit of this is that it can probably done on a tablet or iPhone using very simplistic tools. This would mean that tests would be widely available at low costs and would be a great addition to society. The researches tested the system's performance using a standard measure called area under the curse, otherwise known as integration in mathematics. This test describes the trade off between identifying members of the population that have a particular disorder, this test would limit false positive tests of a disorder. This means that in the medical literature, a diagnostic test with an area under the curve of 0.7 is in general considered accurate enough to be of good use. During three tests in the clinic, the researcher's system ranged between 0.74 to 0.86, which is in general very good and speaks highly of the system that was created. In order to build the new system, Guttag and Jen Gong, students of the graduate level, one in electrical engineering and the other in computer science, used machine learning. This is where a computer searches a large set of training data for any particular patterns that might correspond to specific classifications, in this specific case, they were looking for patterns and classifications of speech and language disorders. This will in turn help clinicians make more precise diagnoses because they have a system of reassurance added on top of their expertise in their respective field. They also indicate that speech impediments that result from an anatomical physical aspect such as a cleft palate, speech disorders and language disorders both have neurological bases.
The benefit of this is that it can probably done on a tablet or iPhone using very simplistic tools. This would mean that tests would be widely available at low costs and would be a great addition to society. The researches tested the system's performance using a standard measure called area under the curse, otherwise known as integration in mathematics. This test describes the trade off between identifying members of the population that have a particular disorder, this test would limit false positive tests of a disorder. This means that in the medical literature, a diagnostic test with an area under the curve of 0.7 is in general considered accurate enough to be of good use. During three tests in the clinic, the researcher's system ranged between 0.74 to 0.86, which is in general very good and speaks highly of the system that was created. In order to build the new system, Guttag and Jen Gong, students of the graduate level, one in electrical engineering and the other in computer science, used machine learning. This is where a computer searches a large set of training data for any particular patterns that might correspond to specific classifications, in this specific case, they were looking for patterns and classifications of speech and language disorders. This will in turn help clinicians make more precise diagnoses because they have a system of reassurance added on top of their expertise in their respective field. They also indicate that speech impediments that result from an anatomical physical aspect such as a cleft palate, speech disorders and language disorders both have neurological bases.
Overall this is an amazing creation and could possibly revolutionize the world of clinical medicine. We can further improve the accuracy of a doctors diagnose with the help of technology. Although this creation is not in its final stages yet, the prime test has been passed, and that is the fact that it works. Now comes the next stage where they have to finalize and release the product.
Reference links:
http://news.mit.edu/2016/automated-screening-childhood-communication-disorders-0922
http://tryengineering.org/sites/default/files/styles/medium/public/majors/169938739-technician-checks-the-voltage.jpg?itok=VASIttqH
http://image.slidesharecdn.com/pptforspeechandlanguage-140821022007-phpapp01/95/speech-and-language-disorders-2-638.jpg?cb=1408587690
http://news.mit.edu/sites/mit.edu.newsoffice/files/styles/news_article_image_top_slideshow/public/images/2016/MIT-Speech-Impairments_0.jpg?itok=ET3cTzEm
Such a fascinating article about the way that computer science could potentially be used to assess risk for communication disorders in children. I could see how this could be a really great way of increasing the efficiency and accuracy of clinical decisions that are typically made by doctors. Without any bias, the computer will be able to objectively measure the child's level of functioning in communication.
ReplyDeleteAs Sami mentioned, using a computer in this context could be so useful because of its ability to diagnose communication disorders in children without a bias. Also, the fact that these tests would be available for a low cost could also greatly impact the amount of children that take them. I am interested to see in what ways this will impact the world of clinical medicine when the product reaches its final stages and is released.
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