On medical application of neural networks trained with various types of data

Karako, Kenji; Chen, Ying; Tang, Wei

doi:10.5582/bst.2018.01264

Cited by 19 publications

(12 citation statements)

References 37 publications

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“…Current artificial intelligence models can achieve diagnostic performance comparable to those of medical experts in various domains 16 – 18 . In the present study, we demonstrated that voice features such as mel-spectrogram can be represented as an image and used as inputs for CNN.…”

Section: Discussionmentioning

confidence: 99%

Identifying individuals with recent COVID-19 through voice classification using deep learning

Suppakitjanusant

Sungkanuparph

Wongsinin

et al. 2021

Sci Rep

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Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.

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Section: Discussionmentioning

confidence: 99%

Identifying individuals with recent COVID-19 through voice classification using deep learning

Suppakitjanusant

Sungkanuparph

Wongsinin

et al. 2021

Sci Rep

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“…Artificial neural network modeling is a powerful tool that can harness the value of large and complex datasets and that, in the era of “big data,” is of increasing interest in medical diagnostics (imaging and histopathology in particular) and prognostics 28,29 . Disease predictions from ANN models are not limited by an understanding of the associated pathophysiology because they are based purely on mathematical relationships.…”

Section: Discussionmentioning

confidence: 99%

An artificial neural network‐based model to predict chronic kidney disease in aged cats

Biourge

Delmotte

Feugier

et al. 2020

Veterinary Internal Medicne

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Background: Chronic kidney disease (CKD) frequently causes death in older cats; its early detection is challenging. Objectives: To build a sensitive and specific model for early prediction of CKD in cats using artificial neural network (ANN) techniques applied to routine health screening data. Animals: Data from 218 healthy cats ≥7 years of age screened at the Royal Veterinary College (RVC) were used for model building. Performance was tested using data from 3546 cats in the Banfield Pet Hospital records and an additional 60 RCV catsall initially without a CKD diagnosis. Methods: Artificial neural network (ANN) modeling used a multilayer feed-forward neural network incorporating a back-propagation algorithm. Clinical variables from single cat visits were selected using factorial discriminant analysis. Independent submodels were built for different prediction time frames. Two decision threshold strategies were investigated. Results: Input variables retained were plasma creatinine and blood urea concentrations, and urine specific gravity. For prediction of CKD within 12 months, the model had accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 88%, 87%, 70%, 53%, and 92%, respectively. An alternative decision threshold increased specificity and PPV to 98% and 87%, but decreased sensitivity and NPV to 42% and 79%, respectively. Conclusions and Clinical Importance: A model was generated that identified cats in the general population ≥7 years of age that are at risk of developing CKD within

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“…Recent reviews of deep learning for medical images highlight the vast amount of emerging research in this field [44,70,112]. On the chest X-rays dataset, various CNN based methods have been suggested.…”

Section: Image Annotationmentioning

confidence: 99%

Multi-label Ranking: Mining Multi-label and Label Ranking Data

Dery¹

2021

Preprint

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We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and recategorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.

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On medical application of neural networks trained with various types of data

Cited by 19 publications

References 37 publications

Identifying individuals with recent COVID-19 through voice classification using deep learning

Identifying individuals with recent COVID-19 through voice classification using deep learning

An artificial neural network‐based model to predict chronic kidney disease in aged cats

Multi-label Ranking: Mining Multi-label and Label Ranking Data

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