Difference between revisions of "Clinical Applications of Machine Learning for Diagnosis"
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Revision as of 00:26, 21 October 2017
Contents
Background
Machine learning is a collection of computer algorithms and techniques that allow computers to learn from data (Kirk, 2017).
Definitions
- Machine learning
- Use of a computerized algorithm to determine associations between factors and an outcome
- Artificial Neural networks
- A computing approach inspired by biological neural networks. It is a network of connected units, similar to neurons and synapses (wikipedia:Artificial neural network).
Examples
Cardiology
Framingham Risk Score for coronary heart disease (Deo, 2015)
This is probably one of the oldest and/or best known examples of machine learning in medicine. It took the data from the original Framing cohort and the Framingham Offspring Study and performed statistical analysis, including linear regression, logistic regression, age-adjustment, to arrive at a scoring mechanism for the prediction of coronary heart disease (Wilson et al., 1998).
Pathology
Diagnosis of pathological specimen is a skill that requires long training, and even then has at times low inter-observer agreement.
Google has tested their deep learning tool "Inception (aka GoogLeNet)" for the pathological detection of metastasized breast cancer. They trained a model that was able to perform at least as well as trained pathologists in most regards. A trade-off between increasing sensitivity and allowing false-positive results was observed (Stumpe and Peng, 2017, Liu et al., 2017).
Dermatology
Esteva et al. report results of training a neural network on skin lesions with their disease labels. The neural network achieved a similar performance to a comparison group of 21 dermatologists for classification of keratinocyte carcinomas versus benign seborrheic keratoses and malignant melanomas versus benign nevi (Esteva et al., 2017).
Ophthalmology
Gulshan et al. (2016) studied the performance of a neural network for identification of diabetic retinopathy. The findings were compared against the assessment by board-certified ophthalmologists. The algorithm achieved a sensitivity of 87-97.5%, and a specificity of 93.4-98.5%, depending on the configuration of the operating point (i.e. trade-off between sensitivity and specificity).
See also
Sources
Kirk, Matthew (2017). Thoughtful Machine Learning with Python. Sebastopol, CA: O'Reilly Media, Inc.
Deo, Rahul C (2015). Machine Learning in Medicine. Circulation;132:1920-1930. DOI: https://doi.org/10.1161/CIRCULATIONAHA.115.001593. Retrieved from: http://circ.ahajournals.org/content/132/20/1920.long
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017). Nature;542:115-118. DOI: 10.1038/nature21056. Retrieved from: https://www.nature.com/nature/journal/v542/n7639/full/nature21056.html
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA;316(22):2402-2410. DOI: 10.1001/jama.2016.17216. Retrieved from: https://jamanetwork.com/journals/jama/article-abstract/2588763
Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, Venugopalan S, Timofeev A, Nelson PQ, Corrado GS, Hipp JD, Peng L, Stumpe MC. Detecting Cancer Metastases on Gigapixel Pathology Images. eprint arXiv:1703.02442. Retrieved from: https://arxiv.org/abs/1703.02442
Stumpe M and Peng L (2017, March 03). Assisting Pathologists in Detecting Cancer with Deep Learning. Retrieved from https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998). Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation;97:1837-1847. DOI: https://doi.org/10.1161/01.CIR.97.18.1837. Retrieved from: http://circ.ahajournals.org/content/97/18/1837.long
Submitted by Thomas Frohwein