AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: XGBoost 10-fold cross-validation: Accuracy 97%, Sensitivity 98%, Specificity 96%, Precision 96%, F1-Score 97%. External validation: Accuracy 95%, Sensitivity 97%, F1-Score 95%

• Primary Outcome: Binary classification of CKD versus healthy controls using LIBS fingernail spectral data processed by XGBoost

• Nnt Or Sensitivity: Sensitivity 98% (cross-validation), 97% (external validation); Specificity 96% (cross-validation, external validation not explicitly stated); Positive and negative likelihood ratios not calculable from reported data

• Confidence Interval: Not reported for any metric

Clinical Application

LIBS instrumentation is specialised laboratory equipment not available in routine clinical or primary care settings. Fingernail collection is non-invasive and acceptable to patients. The analytical pipeline requires spectroscopy expertise and computational infrastructure. Significant barriers to clinical translation exist at this stage of development. CKD affects approximately 1.7 million Australians (Kidney Health Australia data). Current RACGP guidelines recommend eGFR and urine albumin-to-creatinine ratio (uACR) for CKD screening in at-risk populations. LIBS-based fingernail analysis is not TGA-approved, not PBS-listed, and has no RACGP or CARI guideline endorsement. Aboriginal and Torres Strait Islander Australians have disproportionately high CKD burden, and a non-invasive screening tool could theoretically address access barriers in remote settings — but this study provides no evidence to support such application. Substantial further research including prospective cohort studies in representative Australian populations would be required before any regulatory or clinical consideration. Theoretically applicable to adults at risk of CKD requiring non-invasive screening; however, current evidence is insufficient to support clinical application in any population

Abstract

Chronic kidney disease (CKD) is a systemic condition that leads to progressive renal failure and metabolic imbalances that may be detected in the keratinized bio-tissues of the body such as fingernails. Nevertheless, still its early detection is difficult due to the invasive nature of current clinical screening approaches. This research paper provides a precise, non-invasive, and AI-enhanced diagnostic model of CKD screening through the combination of Laser-Induced Breakdown Spectroscopy (LIBS) and the cutting-edge ensemble machine learning to determine the elemental patterns of fingernail biomarkers. In this study, 55 participants with chronic kidney disease and 45 healthy controls were taken as the subjects and their emission spectra were measured with a nanosecond LIBS spectrometer using a laser of a fundamental wavelength @ 1064 nm. To handle the high-dimensional spectral data, Principal Component Analysis (PCA) was used as a feature extraction approach, forming the basis for novel machine-learning models. A sophisticated ensemble learning algorithm specifically Extreme Gradient Boosting (XGBoost) was applied to examine the biochemical change between CKD and control samples and the predictive quality was tested by a relative comparison with a Support Vector Machine (SVM) algorithm. Using 10-fold cross-validation, the XGBoost model outperformed SVM, attaining 97% accuracy, 98% sensitivity, 96% precision and specificity, and a 97% F1-Score. Moreover, external validation on a separate dataset showed that the model is robust and generalizable with 95% accuracy, 97% sensitivity, and an F1-score of 95%. The results conclude that fingernail-based LIBS integrated with ensemble machine learning is a potential and non-invasive instrument for CKD classification.

References

1. 1.Rehan, I., & Aamir, M. (2026). AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. https://doi.org/10.1016/j.saa.2026.127687