Automatic migraine classification using artificial neural networks

Sanchez, Paola A Sanchez; García-González, José Rafael; Ascar, Juan Manuel Rúa

doi:10.12688/f1000research.23181.1

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…Artificial neural networks (ANNs) are one of the most common network architectures commonly used by DL for numerical processing 18 . Based on obtaining prognosis‐related differential genes and pathomics signatures, we constructed three separate binary ANN models: the first was based on differential genes, the second on pathomics signatures, and the third on differential genes and pathomics signatures combined.…”

Section: Methodsmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are one of the most common network architectures commonly used by DL for numerical processing. 18 Based on obtaining prognosis-related differential genes and pathomics signatures, we constructed three separate binary ANN models: the first was based on differential genes, the second on pathomics signatures, and the third on differential genes and pathomics signatures combined. ANN has been used to distinguish CM patients with good prognosis from CM patients with poor prognosis by prognosis-related differential genes and pathomics signatures, and this step was implemented in Python 3.10.…”

Section: Construction Of Binary Models Based On Prognosis-related Dif...mentioning

confidence: 99%

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Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

Li,

Yu,

Tian

et al. 2023

Medical Physics

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BackgroundCutaneous melanoma (CM) is the most common malignant tumor of the skin. Our study aimed to investigate the prognostic value of pathomics signatures for CM by combining pathomics and genomics.PurposeThe purpose of this study was to explore the potential application value of pathomics signatures.MethodsPathology full scans, clinical information, and genomics data for CM patients were downloaded from The Cancer Genome Atlas (TCGA) database. Exploratory data analysis (EDA) was used to visualize patient characteristics. Genes related to a poorer prognosis were screened through differential analysis. Survival analysis was performed to assess the prognostic value of gene and pathomics signatures. Artificial neural network (ANN) models predicted prognosis using signatures and genes. Correlation analysis was used to explore signature‐gene links.ResultsThe clinical traits for 468 CM samples and the genomic data and pathology images for 471 CM samples were obtained from the TCGA database. The EDA results combined with multiple machine learning (ML) models suggested that the top 5 clinical traits in terms of importance were age, biopsy site, T stage, N stage and overall disease stage, and the eight ML models had a precision lower than 0.56. A total of 60 differentially expressed genes were obtained by comparing sequencing data. A total of 413 available quantitative signatures of each pathomics image were obtained with CellProfile software. The precision of the binary classification model based on pathomics signatures was 0.99, with a loss value of 1.7119e‐04. The precision of the binary classification model based on differentially expressed genes was 0.98, with a loss value of 0.1101. The precision of the binary classification model based on pathomics signatures and differentially expressed genes was 0.97, with a loss value of 0.2088. The survival analyses showed that the survival rate of the high‐risk group based on gene expression and pathomics signatures was significantly lower than that of the low‐risk group. A total of 222 pathomics signatures and 51 differentially expressed genes were analyzed for survival with p‐values of less than 0.05. There was a certain correlation between some pathomics signatures and differential gene expression involving ANO2, LINC00158, NDNF, ADAMTS15, and ADGRB3, etc.ConclusionThis study evaluated the prognostic significance of pathomics signatures and differentially expressed genes in CM patients. Three ANN models were developed, and all achieved accuracy rates higher than 97%. Specifically, the pathomics signature‐based ANN model maintained a remarkable accuracy of 99%. These findings highlight the CellProfile + ANN model as an excellent choice for prognostic prediction in CM patients. Furthermore, our correlation analysis experimentally demonstrated a preliminary link between disease quantification and qualitative changes. Among various features, including M stage and treatments received, special attention should be given to age, biopsy site, T stage, N stage, and overall disease stage in CM patients.

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Section: Methodsmentioning

confidence: 99%

Section: Construction Of Binary Models Based On Prognosis-related Dif...mentioning

confidence: 99%

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

Li,

Yu,

Tian

et al. 2023

Medical Physics

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“…Weitere Studien zeigten, dass je nach angewandter KI-Methode, die Diagnose einer Migräne bzw. die Unterteilung in einzelne Migräneformen anhand der Anamnese und Patientenfragebogen mit einer Genauigkeit von bis zu 98 % möglich ist 61 .…”

Section: Künstliche Intelligenz In Teilgebieten Der Neurologieunclassified

Künstliche Intelligenz in der Neurologie

Ernst,

Rémi

2023

Nervenheilkunde

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ZUSAMMENFASSUNGKünstliche Intelligenz (KI) ist spätestens seit der Veröffentlichung von ChatGPT in aller Munde. Die Grundlage eines jeden KI-Modells ist die Analyse von Daten. In der Neurologie sind aufgrund der Digitalisierung ausreichend große Datenmengen vorhanden, um mittels KI analysiert werden zu können. Dieser Artikel soll einen Überblick über KI-Modelle sowie aktuelle Forschungen und Anwendungen in der Neurologie geben. Mögliche Probleme in der Integration der KI in den klinischen Alltag werden beleuchtet und ein Ausblick auf die Zukunft wird versucht.

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Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data

et al. 2023

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IntroductionMigraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world’s population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients.MethodsThis research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training.ResultsThe results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification.DiscussionThis method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA.

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Automatic migraine classification using artificial neural networks

Cited by 4 publications

References 56 publications

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

Exploring and validating the prognostic value of pathomics signatures and genomics in patients with cutaneous melanoma based on bioinformatics and deep learning

Künstliche Intelligenz in der Neurologie

Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data

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