ArticleNPJ digital medicine2023
Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Lactobacillus Vaginal Tablets in Preventing Recurrence of Bacterial Vaginosis: A Systematic Review and Meta-Analysis.Probiotics and antimicrobial proteins · 2026Review
- Causal Graphical Model of Bacterial Vaginosis in Pregnant Women.Diseases (Basel, Switzerland) · 2025Article
- Innovative engineering approaches to model host-microbiome interactions in vitro.Advanced drug delivery reviews · 2025Review
- Natural Molecules, Nutraceuticals, and Engineered Nanosystems: A Comprehensive Strategy for CombatingMicroorganisms · 2025Review
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis.Scientific reports · 2025Article
- Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.mSystems · 2025Article
- Population-Level Predictive Variation in Machine Learning Diagnosis of Symptomatic Bacterial Vaginosis.npj women's health · 2025Article
- Article
- Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Comparative analysis of clinical feature-based machine learning models for predicting myofascial pelvic pain syndrome: a single-center retrospective study.Frontiers in medicine · 2025Article
- Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ digital medicine · 2024Review
- How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well among ethnic groups. This is alarming for women's health, as there are already existing health disparities that vary by ethnicity. Bacterial Vaginosis (BV) is a common vaginal syndrome among women of reproductive age and has clear diagnostic differences among ethnic groups. Here, we investigate the ability of four ML algorithms to diagnose BV. We determine the fairness in the prediction of asymptomatic BV using 16S rRNA sequencing data from Asian, Black, Hispanic, and white women. General purpose ML model performances vary based on ethnicity. When evaluating the metric of false positive or false negative rate, we find that models perform least effectively for Hispanic and Asian women. Models generally have the highest performance for white women and the lowest for Asian women. These findings demonstrate a need for improved methodologies to increase model fairness for predicting BV.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.