ArticleBiomedical optics express2018
Analysis and classification of kidney stones based on Raman spectroscopy.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.Current urology reports · 2021Pooled it
- Cementum Attachment Protein-Derived Peptides Modulate Brushite and Calcium Oxalate Crystallization In Vitro.Journal of peptide science : an official publication of the European Peptide Society · 2026Article
- An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images.Scientific reports · 2025Article
- Article
- Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves ofSensors (Basel, Switzerland) · 2024Article
- DHM/SERS reveals cellular morphology and molecular changes during iPSCs-derived activation of astrocytes.Biomedical optics express · 2024Article
- Kidney stone growth through the lens of Raman mapping.Scientific reports · 2024Article
- Chemical Studies of Multicomponent Kidney Stones Using the Modern Advanced Research Methods.Molecules (Basel, Switzerland) · 2023Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- Real-time and accurate calibration detection of gout stones based on terahertz and Raman spectroscopy.Frontiers in bioengineering and biotechnology · 2023Article
- Classification and Prediction by Pigment Content in Lettuce (Plants (Basel, Switzerland) · 2022Article
- Identification of Uric Acid Crystals Accumulation in Human and Animal Tissues Using Combined Morphological and Raman Spectroscopy Analysis.Diagnostics (Basel, Switzerland) · 2022Article
- Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms.ACS omega · 2022Article
- In Situ Identification of Unknown Crystals in Acute Kidney Injury Using Raman Spectroscopy.Nanomaterials (Basel, Switzerland) · 2022Article
- Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning.Biomedical optics express · 2021Article
- Human kidney stones: a natural record of universal biomineralization.Nature reviews. Urology · 2021Review
- Application of Surface-Enhanced Raman Spectroscopy and Machine Learning Omics Techniques in the Progression Assessment of Autosomal Dominant Polycystic Kidney Disease.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of patients with kidney stones worldwide is increasing, and it is particularly important to facilitate accurate diagnosis methods. Accurate analysis of the type of kidney stones plays a crucial role in the patient's follow-up treatment. This study used microscopic Raman spectroscopy to analyze and classify the different mineral components present in kidney stones. There were several Raman changes observed for the different types of kidney stones and the four types were oxalates, phosphates, purines and L-cystine kidney stones. We then combined machine learning techniques with Raman spectroscopy. KNN and SVM combinations with PCA (PCA-KNN, PCA-SVM) methods were implemented to classify the same spectral data set. The results show the diagnostic accuracies are 96.3% for the PCA-KNN and PCA-SVM methods with high sensitivity (0.963, 0.963) and specificity (0.995,0.985). The experimental Raman spectra results of kidney stones show the proposed method has high classification accuracy. This approach can provide support for physicians making treatment recommendations to patients with kidney stones.
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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.