Deep Learning Models are Predicting and Diagnosing Alzheimer’s Disease with Neuroimaging

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Alzheimer’s disease remains one of the most challenging diseases to recognize in its early stages. It often takes an experienced clinician to make a proper diagnosis. As there are only some identifying factors for the disease, finding new methods that could be used for creating a diagnosis comes down to future technology in the medical field.

Through the use of a deep learning model, there have been advancements made through positron emission tomography. By examining using nuclear medicine and 18F-fluorodeoxyglucose is possible to create pattern recognition leading to an improved level of diagnosis. The problem with this type of imaging previously is that it was often difficult to use pattern recognition and identify more than a qualitative read. Deep learning is assisting within the radiology community to help with the increasing complexity of this type of imaging data.

The San Francisco Department of radiology teamed up with a biomedical imaging staff to create a deep learning solution that could diagnose patients utilizing this type of imaging. Deep learning is one of the ideal solutions for this problem because it can create a strong capture for the processing of each image. The technology has been featured in publications such as in Radiology.

This type of research could have some incredible implications for detecting Alzheimer’s early which would relate to future patient care. Through the use of biochemical testing and imaging tests, it’s possible that the algorithm could help to manage intervention for early therapeutic use. Detecting a series of patterns and features are already starting to achieve higher results and diagnosis.

Using this imaging technology, the research team achieved 82% level of specificity in the prediction of Alzheimer’s disease at an average of 75 months for the diagnosis. As the algorithm continues to learn, it could eventually bring the diagnosis accuracy up and the ability of the old rhythm to diagnose at an earlier rate as well.

These types of deep learning models could eventually improve the overall accuracy of any neural imaging. If deep learning algorithms can be applied to the diagnosis of a complicated disease like Alzheimer’s, this could eventually have future applications and diagnosing other types of brain conditions. With an early diagnosis, it’s possible to start intervening with therapy as soon as possible which can lead to a slowed progression of the disease.

Deep learning models could eventually start to bite people time in their diagnosis with Alzheimer’s disease. An early diagnosis could slow down the progression and ensure that a person could potentially live years longer with Alzheimer’s through proper treatment.

As the deep learning algorithm is exposed to more data sets, it will only be a matter of time before the program can improve in its accuracy and before final diagnostic clarification can be improved in most individuals. The utilization of deep learning out rhythms could continue to find its way into radiology applications worldwide and could eventually lead to massive improvements with patient care.


Source: The information used in this article is from https://radiology.ucsf.edu/blog/deep-learning-model-predicts-diagnosis-alzheimer%E2%80%99s-disease-using-18f-fdg-pet

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