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John Paul Cook

Charles Bonnet Syndrome and Implications for Digital Image Processing

Those of you who know me or have read my biography are aware that I live in two worlds, information technology and healthcare. This post is about image processing in the brain and in a computer. In particular, this discussion is about what happens when there is insufficient data. I think there are some lessons from healthcare that can be applied to information technology.

First, some background. Charles Bonnet was a Swiss naturalist (scientist who studies the natural world) who in 1760 wrote about his 89-year old grandfather’s visual hallucinations. The grandfather had cataracts which rendered him almost blind. In other words, his retinas were sending insufficient data to his brain. He reported seeing people, animals, and objects. This syndrome was named after Charles Bonnet and is also known as visual release hallucinations. Retinas and digital imaging sensors are alike in that they generate signals that must be processed to be understood. Processing is not error free. When the retina receives less input, it’s similar to trying to make sense of a a very pixelated image.

When discussing machine learning, we mention training a model. Data is fed into a algorithm and a model is trained. New data such a digital image is submitted to the model. If an approximate match is found, the model classifies and categorizes the new image. The brain does something similar. Consider the case of a person with retinal damage caused by age related macular generation, diabetic retinopathy, glaucoma, or some other pathology. If the person is able to see brown, white, and black next to each other, the trained model in the brain may find a match on a beagle. The brain says brown, white, and black must be a beagle so I’ll put a beagle into the person’s field of view. Putting this into information technology terms, a beagle might be algorithmically correct based on the input data, but if there isn’t a beagle present, the outcome is a false positive.

It’s important to understand that this discussion is about visual hallucinations in a person without any psychiatric problems. In fact, the people who have this syndrome are aware that the hallucinations are not real. In other words, they have insight into what is happening. The take away is that no matter how good our digital image processing systems are, we still need oversight from a thinking human being who asks and answers the question: Does this make sense? The Indian ophthalmologist G. J. Menon published a framework for evaluating visual hallucinations. In his framework, the presence or absence of insight into the hallucination is very significant. I’m suggesting that our work isn’t done when a model is trained. We need to consider developing problem  specific frameworks for critically appraising the results of machine learning.

It’s important to consider the consequences of incorrect processing. There are reports of people with Charles Bonnet Syndrome fretting about their hallucinations and fearing they may be losing their grip on their sanity. They may not bring it up because of a fear of no longer being allowed to live independently in case they are diagnosed as psychiatrically ill. It’s a great relief to these patients when they finally find out that their hallucinations are actually normal. How family members and healthcare professionals treat a patient is quite different when they think a person is psychiatrically normal instead of abnormal. Following procedures and jumping to easy conclusions can have devastating consequences for people.

You can read more about Charles Bonnet Syndrome at the website of the Charles Bonnet Syndrome Foundation. There are similar auditory hallucinations in psychiatrically normal people. Once again, the presence of insight about the hallucinations is significant. We need critical human thinking skills providing the sanity checks for signal processing.

Published Sunday, April 30, 2017 3:56 PM by John Paul Cook

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About John Paul Cook

John Paul Cook is a database and Azure specialist in Houston. He previously worked as a Data Platform Solution Architect in Microsoft's Houston office. Prior to joining Microsoft, he was a SQL Server MVP. He is experienced in SQL Server and Oracle database application design, development, and implementation. He has spoken at many conferences including Microsoft TechEd and the SQL PASS Summit. He has worked in oil and gas, financial, manufacturing, and healthcare industries. John is also a Registered Nurse currently studying to be a psychiatric nurse practitioner. Contributing author to SQL Server MVP Deep Dives and SQL Server MVP Deep Dives Volume 2. Connect on LinkedIn

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