Dataset Information

Automatic classification of signal regions in ¹H Nuclear Magnetic Resonance spectra.

ABSTRACT: The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in ¹H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental ¹H NMR spectra.

SUBMITTER: Fischetti G

PROVIDER: S-EPMC9874632 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Automatic classification of signal regions in <sup>1</sup>H Nuclear Magnetic Resonance spectra.

Fischetti Giulia G Schmid Nicolas N Bruderer Simon S Caldarelli Guido G Scarso Alessandro A Henrici Andreas A Wilhelm Dirk D

Frontiers in artificial intelligence 20230111

The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in <sup>1</sup>H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. W ...[more]

PMID: 36714208

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Project description:BackgroundArrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed according to the Task Force Criteria (TFC) in which cardiovascular magnetic resonance (CMR) imaging plays an important role. Our study aims to apply an automatic deep learning-based segmentation for right and left ventricular CMR assessment and evaluate this approach for classification of the CMR TFC.MethodsWe included 227 subjects suspected of ARVC who underwent CMR. Subjects were classified into (1) ARVC patients fulfilling TFC; (2) at-risk family members; and (3) controls. To perform automatic segmentation, a Bayesian Dilated Residual Neural Network was trained and tested. Performance of automatic versus manual segmentation was assessed using Dice-coefficient and Hausdorff distance. Since automatic segmentation is most challenging in basal slices, manual correction of the automatic segmentation in the most basal slice was simulated (automatic-basal). CMR TFC calculated using manual and automatic-basal segmentation were compared using Cohen's Kappa (κ).ResultsAutomatic segmentation was trained on CMRs of 70 subjects (39.6 ± 18.1 years, 47% female) and tested on 157 subjects (36.9 ± 17.6 years, 59% female). Dice-coefficient and Hausdorff distance showed good agreement between manual and automatic segmentations (≥ 0.89 and ≤ 10.6 mm, respectively) which further improved after simulated correction of the most basal slice (≥ 0.92 and ≤ 9.2 mm, p < 0.001). Pearson correlation of volumetric and functional CMR measurements was good to excellent (automatic (r = 0.78-0.99, p < 0.001) and automatic-basal (r = 0.88-0.99, p < 0.001) measurements). CMR TFC classification using automatic-basal segmentations was comparable to manual segmentations (κ 0.98 ± 0.02) with comparable diagnostic performance.ConclusionsCombining automatic segmentation of CMRs with correction of the most basal slice results in accurate CMR TFC classification of subjects suspected of ARVC.

Dataset Information

Automatic classification of signal regions in ¹H Nuclear Magnetic Resonance spectra.

Publications

Automatic classification of signal regions in <sup>1</sup>H Nuclear Magnetic Resonance spectra.

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Dataset Information

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra.

Publications

Automatic classification of signal regions in <sup>1</sup>H Nuclear Magnetic Resonance spectra.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Automatic classification of signal regions in ¹H Nuclear Magnetic Resonance spectra.