The review, conducted by Hamidah Mabruk Alodayni, Fatimah Alodaini, Alun H. Davies and Pasha Normahani, followed PRISMA-ScR guidelines and searched MEDLINE and Embase from 1946 through November 2025. The result is both encouraging and sobering. Seven studies met the inclusion criteria, all published between 2017 and 2022—and every single one came from the same French vascular research group that developed the system. That single-source evidence base colors everything the review found, even as the individual results are strikingly positive.

The Saint-Bonnet classification was born out of frustration. The French College of Teachers in Vascular Medicine (CEMV) recognized that when clinicians were left to describe waveforms in free text, the vocabulary exploded. In one study, 110 vascular physicians describing 20 anonymized waveforms without any framework produced an average of 92.8 distinct descriptive expressions. With the Saint-Bonnet system, that collapsed to just 6.1. The system itself is elegantly simple: eight ordered morphological types, labeled N, A, B, CD, E, O, FA and U, running from normal triphasic flow all the way to the complete absence of signal, each stage mapped to a defined level of haemodynamic severity.

The performance data are impressive. In a multicentre prospective study of 1,033 Doppler waveforms, the Saint-Bonnet system successfully categorized 98.2 percent of them, significantly outperforming the alternative Spronk (91.3 percent) and Descotes and Cathignol (76.8 percent) classifications. Inter-rater reliability among clinicians was moderate, with a kappa of roughly 0.55—decent but not perfect, and varying considerably across individual categories, from 0.24 to 0.87. The reviewers suggest this variability may reflect the system’s youth and the training it requires rather than any inherent flaw.

More tantalizing still are the clinical correlations. A cross-sectional study of 186 symptomatic patients found that Saint-Bonnet waveform grade was independently associated with maximal walking distance, performing even better than the ankle-brachial index (ABI) in multivariable models—a sign that the shape of the waveform itself carries meaningful functional information. Meanwhile, a prospective cohort of 188 patients showed that using the waveform grade to select which ankle artery to measure for ABI produced near-perfect agreement with guideline-standard measurements, with kappa values reaching 1.00 when the two ankle arteries displayed different waveform grades.

The system is also proving compatible with artificial intelligence. In a machine-learning proof of concept, a deep convolutional neural network trained on Saint-Bonnet-labeled waveform images achieved 83.7 percent accuracy and a kappa of 0.79, approaching the human expert consensus value of 0.83. The reviewers caution, however, that this demonstrates technical reproducibility—teaching a machine to mimic the classification—rather than independent validation of the classification’s diagnostic accuracy, since the training ground truth was the classification itself.

To assess methodological quality, the team applied two validated appraisal instruments, AGREE II and AGREE-REX, to the CEMV guideline document that formally codified the classification. The results reveal a striking split personality. AGREE II scores were excellent for Clarity of Presentation (97 percent) and Scope and Purpose (83 percent), confirming that the system is well-defined and beautifully laid out. But Rigour of Development scored just 44 percent and Applicability 40 percent, pointing to an absence of systematic evidence synthesis, weak implementation planning, and minimal conflict-of-interest reporting. Stakeholder Involvement reached 69 percent and Editorial Independence only 50 percent. Notably, no patient involvement appeared anywhere in the system’s development.

AGREE-REX told the opposite story: high scores for Clinical Applicability (6.0) and Implementability (5.5), with Values and Preferences at a moderate 4.3. Clinicians, in other words, find the system genuinely useful and easy to deploy. The reviewers observe that this combination—low developmental rigour paired with high clinical utility—is a familiar pattern for expert-driven frameworks promoted before independent validation, a phenomenon not unique to vascular medicine.

The central caveat is impossible to ignore. Because all seven supporting studies originated from a single, non-independent French group, the entire evidence base is internally consistent but externally unreplicated. No study has tested the classification in other healthcare systems, among international sonographers, or in longitudinal cohorts tracking whether waveform grade predicts clinical outcomes over time. The reviewers call for multicentre international validation, head-to-head comparisons across different healthcare contexts, prognostic studies, and integration into AI-assisted Doppler platforms as the key research priorities ahead.

The verdict, then, is nuanced: the Saint-Bonnet classification is a clinically logical and remarkably practical framework that tames decades of terminological anarchy and shows genuine promise for both bedside assessment and automated interpretation. Its low AGREE II scores reflect gaps in documentation and governance rather than evidence against its clinical performance. But until independent, multicentre studies replicate the French findings, the reviewers conclude, the system should be regarded as promising rather than established—and broader adoption should wait for that confirmation.

Subject of Research: Evidence appraisal of the Saint-Bonnet Doppler waveform classification for peripheral arterial disease

Article Title: Saint-Bonnet Doppler waveform classification system: a scoping review and evidence-based appraisal using AGREE II and AGREE-REX

Article References: Alodayni, H. M., Alodaini, F., Davies, A. H., & Normahani, P. (2026). Saint-Bonnet Doppler waveform classification system: a scoping review and evidence-based appraisal using AGREE II and AGREE-REX. Frontiers in Cardiovascular Medicine, 13, Article 1870182. https://doi.org/10.3389/fcvm.2026.1870182

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1870182

Keywords: Doppler waveform, Saint-Bonnet classification, peripheral arterial disease, duplex ultrasound, AGREE II, AGREE-REX, scoping review, vascular medicine, ankle-brachial index, machine learning, inter-rater reliability, clinical guidelines