Limits...
Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging.

Iwabuchi SJ, Liddle PF, Palaniyappan L - Front Psychiatry (2013)

Bottom Line: Seven Tesla classifiers outperformed the 3-T classifiers with accuracy reaching as high as 77% for the 7-T GM classifier compared to 66.6% for the 3-T GM classifier.Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making.We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches.

View Article: PubMed Central - PubMed

Affiliation: Division of Psychiatry, Centre for Translational Neuroimaging in Mental Health, University of Nottingham , Nottingham , UK.

ABSTRACT
Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n = 19) from healthy controls (n = 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects. Seven Tesla classifiers outperformed the 3-T classifiers with accuracy reaching as high as 77% for the 7-T GM classifier compared to 66.6% for the 3-T GM classifier. Furthermore, diagnostic odds ratio (a measure that is not affected by variations in sample characteristics) and number needed to predict (a measure based on Bayesian certainty of a test result) indicated superior performance of the 7-T classifiers, whereby for each correct diagnosis made, the number of patients that need to be examined using the 7-T GM classifier was one less than the number that need to be examined if a different classifier was used. Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making. We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.

No MeSH data available.


Related in: MedlinePlus

Discrimination maps for each classifier at a threshold of 30% of the maximum positive and negative weight values, superimposed onto a standard brain template provided by MRICron. (A) 3 T GM, (B) 3 T WM, (C) 7 T GM, and (D) 7 T WM. Color bar represents the minimum and maximum thresholded weights for each classifier.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3756305&req=5

Figure 2: Discrimination maps for each classifier at a threshold of 30% of the maximum positive and negative weight values, superimposed onto a standard brain template provided by MRICron. (A) 3 T GM, (B) 3 T WM, (C) 7 T GM, and (D) 7 T WM. Color bar represents the minimum and maximum thresholded weights for each classifier.

Mentions: Each voxel carries a certain weight value signifying its contribution toward the classification function. This value can be positive or negative, where a positive value would represent a higher weighted average for class one (controls group), while a negative value would mean the weighted average was higher for class two (patient group). Since classifiers use a multivariate approach, and therefore discriminations are based on the global spatial pattern, local inferences should never be made in regards to the weights. For each classifier, in line with Mourao-Miranda et al. (16), we set a threshold of 30% of the maximum positive and negative weight values to generate a spatial representation of the regions that most contributed to the group discrimination. These maps are illustrated in Figure 2.


Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging.

Iwabuchi SJ, Liddle PF, Palaniyappan L - Front Psychiatry (2013)

Discrimination maps for each classifier at a threshold of 30% of the maximum positive and negative weight values, superimposed onto a standard brain template provided by MRICron. (A) 3 T GM, (B) 3 T WM, (C) 7 T GM, and (D) 7 T WM. Color bar represents the minimum and maximum thresholded weights for each classifier.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756305&req=5

Figure 2: Discrimination maps for each classifier at a threshold of 30% of the maximum positive and negative weight values, superimposed onto a standard brain template provided by MRICron. (A) 3 T GM, (B) 3 T WM, (C) 7 T GM, and (D) 7 T WM. Color bar represents the minimum and maximum thresholded weights for each classifier.
Mentions: Each voxel carries a certain weight value signifying its contribution toward the classification function. This value can be positive or negative, where a positive value would represent a higher weighted average for class one (controls group), while a negative value would mean the weighted average was higher for class two (patient group). Since classifiers use a multivariate approach, and therefore discriminations are based on the global spatial pattern, local inferences should never be made in regards to the weights. For each classifier, in line with Mourao-Miranda et al. (16), we set a threshold of 30% of the maximum positive and negative weight values to generate a spatial representation of the regions that most contributed to the group discrimination. These maps are illustrated in Figure 2.

Bottom Line: Seven Tesla classifiers outperformed the 3-T classifiers with accuracy reaching as high as 77% for the 7-T GM classifier compared to 66.6% for the 3-T GM classifier.Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making.We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches.

View Article: PubMed Central - PubMed

Affiliation: Division of Psychiatry, Centre for Translational Neuroimaging in Mental Health, University of Nottingham , Nottingham , UK.

ABSTRACT
Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n = 19) from healthy controls (n = 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects. Seven Tesla classifiers outperformed the 3-T classifiers with accuracy reaching as high as 77% for the 7-T GM classifier compared to 66.6% for the 3-T GM classifier. Furthermore, diagnostic odds ratio (a measure that is not affected by variations in sample characteristics) and number needed to predict (a measure based on Bayesian certainty of a test result) indicated superior performance of the 7-T classifiers, whereby for each correct diagnosis made, the number of patients that need to be examined using the 7-T GM classifier was one less than the number that need to be examined if a different classifier was used. Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making. We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.

No MeSH data available.


Related in: MedlinePlus