Recommandations du Collège des enseignants de médecine vasculaire (CEMV) : analyse des flux artériels Doppler (classification de Saint-Bonnet simplifiée)

Boulon, Carine; Desormais, Iléana; Lacroix, Philippe; Bressollette, L; Guilmot, J.-L.; Hello, C. Le; Sevestre, Marie‐Antoinette; Pernod, Gilles; Constans, J.; Boissier, C.; Bura-Rivière, A.

doi:10.1016/j.jdmv.2018.05.002

Cited by 5 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, acknowledging significant heterogeneity in waveform interpretation, there has been an attempt to standardise key definitions and descriptors of waveform morphology by expert consensus. 21,22 However, it remains the case that waveform morphology is complex and is dependent on location in the arterial tree as well as severity of disease. Waveform interpretation will therefore likely remain challenging and will continue to be associated with significant inter-observer variation.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study

et al. 2022

View full text Add to dashboard Cite

Background: Point-of-care duplex ultrasound has emerged as a promising test for the diagnosis of peripheral artery disease (PAD). However, the interpretation of morphologically diverse Doppler arterial spectral waveforms is challenging and associated with wide inter-observer variation. The aim of this study is to evaluate the utility of machine learning techniques for the diagnosis of PAD from Doppler arterial spectral waveforms sampled at the level of the ankle in patients with diabetes. Methods: In two centres, 590 Doppler arterial spectral waveform images (PAD 369, no-PAD 221) from 305 patients were prospectively collected. Doppler arterial spectral waveform signals were reconstructed. Blinded full lower-limb reference duplex ultrasound results were used to label waveform according to PAD status (i.e., PAD, no-PAD). Statistical metrics and multiscale wavelet variance were extracted as discriminatory features. A long short-term memory (LSTM) network was used for the classification of raw signals, and logistic regression (LR) and support vector machines (SVM) were used for classification of extracted features. Signals and feature vectors were randomly divided into training (80%) and testing (20%) sets. Results: The highest overall accuracy was achieved using a logistic regression model with a combination of statistical and multiscale wavelet variance features, with 88% accuracy, 92% sensitivity, and 82% specificity. The area under the receiver operating characteristics curve (AUC) was 0.93. Conclusion: We have constructed a machine learning algorithm with high discriminatory ability for the diagnosis of PAD using Doppler arterial spectral waveforms sampled at the ankle vessels.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The simplified Saint-Bonnet classification ( Figure 1 ) was chosen as the reference classification for two reasons: (i) The College of the French Vascular Medicine Teachers (Collège des Enseignants de Médecine Vasculaire, CEMV) recommends the use of the simplified Saint-Bonnet Classification in its most recent guidelines [ 9 , 26 ] and (ii) The simplified Saint-Bonnet Classification categories describe extensive types of arterial Doppler flow waveform representing different progressing grades of PAD severity [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

Use of a Pre-Trained Neural Network for Automatic Classification of Arterial Doppler Flow Waveforms: A Proof of Concept

Guilcher

Lanéelle

2021

JCM

View full text Add to dashboard Cite

Background: Arterial Doppler flow waveform analysis is a tool recommended for the management of lower extremity peripheral arterial disease (PAD). To standardize the waveform analysis, classifications have been proposed. Neural networks have shown a great ability to categorize data. The aim of the present study was to use an existing neural network to evaluate the potential for categorization of arterial Doppler flow waveforms according to a commonly used classification. Methods: The Pareto efficient ResNet-101 (ResNet-101) neural network was chosen to categorize 424 images of arterial Doppler flow waveforms according to the Simplified Saint-Bonnet classification. As a reference, the inter-operator variability between two trained vascular medicine physicians was also assessed. Accuracy was expressed in percentage, and agreement was assessed using Cohen’s Kappa coefficient. Results: After retraining, ResNet-101 was able to categorize waveforms with 83.7 ± 4.6% accuracy resulting in a kappa coefficient of 0.79 (0.75–0.83) (CI 95%), compared with a kappa coefficient of 0.83 (0.79–0.87) (CI 95%) between the two physicians. Conclusion: This study suggests that the use of transfer learning on a pre-trained neural network is feasible for the automatic classification of images of arterial Doppler flow waveforms.

show abstract

“…Assim, para avaliação qualitativa foi levada em conta a morfologia da onda espectral Doppler, seguindo as Recomendações e Instruções do Colégio de Professores de Medicina Vascular da França (CEMV) que analisaram diferentes formas de onda Doppler arterial (Mahé et al, 2018). Para classificação gráfica do fluxo sanguíneo foi utilizado o modelo de classificação simplificada de Saint-Bonnet ) mapeando a amplitude da velocidade sistólica máxima, onda diastólica precoce, e a onda diastólica final (Figura 6).…”

Section: Diaunclassified

Influência da fotobiomodulação com diodo emissor de luz (LED) sobre a dor e fluxo sanguíneo em indivíduos com neuropatia diabética

Sapalo¹

View full text Add to dashboard Cite

Introduction: Vascular and neuronal impairment is one of the reasons for early hospitalization of diabetic patients, mainly affecting the extremities of the lower limbs. Photobiomodulation is a non-pharmacological therapeutic line that has been increasingly used in recent years, and its effects are attributed to the modulatory capacity to increase the level of cellular energy metabolism. Objective: to evaluate the influence of photobiomodulation with light emitting diode (LED) in the red (V), infrared (IV) and associated (V + IV) spectrum on pain and blood flow in patients with diabetic neuropathy. Methods: The study sample consisted of 58 patients, aged between 45 and 70 years, totaling 116 evaluated lower limbs. The study procedure was carried out in 5 days. On the first day (pre-treatment), patients were assessed for vascular impairment and the level of neuropathic pain. From the second to the fourth day, photobiomodulation was applied directly to the skin of the patients' legs, using a blanket (33x42cm2) of red LEDs (620nm; 52.86 mW / cm²), infrared (940nm; 33.7 mW / cm²) or associated (620nm; 52.86 mW / cm² and 940nm; 33.7 mW / cm²) during 1'30 ". On the fifth day, patients underwent reassessment. Results: Regarding the assessment of neuropathic pain, there was an average reduction (p <0.05) between 4.7 and 5.5 points in the Douleur Neuropathique 4 Questions in all groups irradiated with LED, with a large clinical effect size. In the blood flow assessment, there were no statistically significant changes (p> 0.05), despite the moderate effect size. Conclusion: Photobiomodulation therapy with light emitting diode (LED) has been shown to be effective in relieving neuropathic pain in the lower limbs of diabetic patients, with a clinical effect in improving blood flow.

show abstract

Recommandations du Collège des enseignants de médecine vasculaire (CEMV) : analyse des flux artériels Doppler (classification de Saint-Bonnet simplifiée)

Cited by 5 publications

References 16 publications

Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study

Machine learning-based classification of arterial spectral waveforms for the diagnosis of peripheral artery disease in the context of diabetes: A proof-of-concept study

Use of a Pre-Trained Neural Network for Automatic Classification of Arterial Doppler Flow Waveforms: A Proof of Concept

Influência da fotobiomodulação com diodo emissor de luz (LED) sobre a dor e fluxo sanguíneo em indivíduos com neuropatia diabética

Contact Info

Product

Resources

About