Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8534 |
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| author | Antonio Currà Riccardo Gasbarrone Davide Gattabria Nicola Luigi Bragazzi Giuseppe Bonifazi Silvia Serranti Paolo Missori Francesco Fattapposta Carlotta Manfredi Andrea Maffucci Luca Puce Lucio Marinelli Carlo Trompetto |
| author_facet | Antonio Currà Riccardo Gasbarrone Davide Gattabria Nicola Luigi Bragazzi Giuseppe Bonifazi Silvia Serranti Paolo Missori Francesco Fattapposta Carlotta Manfredi Andrea Maffucci Luca Puce Lucio Marinelli Carlo Trompetto |
| author_sort | Antonio Currà |
| collection | DOAJ |
| description | This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R<sup>2</sup><sub>CV</sub> = 0.980; FSS: R<sup>2</sup><sub>CV</sub> = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. |
| format | Article |
| id | doaj-art-5de63aab9d5f455f8e37a06236c0f43a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5de63aab9d5f455f8e37a06236c0f43a2025-08-20T04:00:54ZengMDPI AGApplied Sciences2076-34172025-07-011515853410.3390/app15158534Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple SclerosisAntonio Currà0Riccardo Gasbarrone1Davide Gattabria2Nicola Luigi Bragazzi3Giuseppe Bonifazi4Silvia Serranti5Paolo Missori6Francesco Fattapposta7Carlotta Manfredi8Andrea Maffucci9Luca Puce10Lucio Marinelli11Carlo Trompetto12Neurology Unit, AOU Policlinico Umberto I, Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, ItalyResearch and Service Center for Sustainable Technological Innovation (Ce.R.S.I.Te.S.), Sapienza University of Rome, 04100 Latina, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, 00184 Rome, ItalyDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genova, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, 00184 Rome, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza University of Rome, 00184 Rome, ItalyNeurosurgery Unit, AOU Policlinico Umberto I, Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, ItalyNeurology Unit, AOU Policlinico Umberto I, Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, ItalyNeurology Unit, AOU Policlinico Umberto I, Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, ItalyNeurology Unit, AOU Policlinico Umberto I, Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genova, ItalyDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genova, ItalyDepartment of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genova, ItalyThis study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R<sup>2</sup><sub>CV</sub> = 0.980; FSS: R<sup>2</sup><sub>CV</sub> = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging.https://www.mdpi.com/2076-3417/15/15/8534short-wave infrared (SWIR) spectroscopymultiple sclerosis (MS)reflectance spectrachemometric analysisin vivo analysis |
| spellingShingle | Antonio Currà Riccardo Gasbarrone Davide Gattabria Nicola Luigi Bragazzi Giuseppe Bonifazi Silvia Serranti Paolo Missori Francesco Fattapposta Carlotta Manfredi Andrea Maffucci Luca Puce Lucio Marinelli Carlo Trompetto Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis Applied Sciences short-wave infrared (SWIR) spectroscopy multiple sclerosis (MS) reflectance spectra chemometric analysis in vivo analysis |
| title | Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis |
| title_full | Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis |
| title_fullStr | Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis |
| title_full_unstemmed | Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis |
| title_short | Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis |
| title_sort | machine learning for the photonic evaluation of cranial and extracranial sites in healthy individuals and in patients with multiple sclerosis |
| topic | short-wave infrared (SWIR) spectroscopy multiple sclerosis (MS) reflectance spectra chemometric analysis in vivo analysis |
| url | https://www.mdpi.com/2076-3417/15/15/8534 |
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