Table 1 Summary of existing works.
Author (Year) | Target | Technology | Detection Method | Performance | Application Area | Limitation |
|---|---|---|---|---|---|---|
Kodogiannis et al.41 | UTI detection | Electronic nose + ENRBF neural model | Volatile compound analysis | 100% accuracy | Point-of-care diagnostics | Small sample size; clinical validation needed |
Sharma & Sharan (2015) | Glucose | Photonic crystal-based biosensor | Refractive index simulation | High sensitivity | Lab-on-chip integration | Simulated only; lacks clinical validation |
Seo et al.54 | UTI detection | Paper-based colorimetric + BLE | Nitrite detection | 4 mg/L limit | Wearable real-time monitoring | Focus on nitrite alone; limited biomarker range |
Roux-Dalvai et al.50 | Bacterial ID | MALDI-TOF MS | Proteomic fingerprinting | Rapid & accurate | Clinical diagnostic integration | Dependent on high-cost equipment |
Saberi et al.51 | Potassium | G-quadruplex aptasensor | Fluorescence recovery | 12.3 nM | Ion-selective alternative | Performance under clinical conditions not tested |
McKay et al.45 | UTI biomarkers | Lens-free holographic imaging | RBC, WBC, E. coli | R² > 0.99 | Portable low-resource use | Lacks full clinical integration; lab comparison limited |
Massy et al.44 | CKD progression | Machine learning + peptidomics | CE-MS analysis | Outperformed standards | Risk stratification | High-cost; requires specialized instruments |
Durán Acevedo et al. 24 | Prostate cancer | Electronic tongue + ML | Urine pattern recognition | 92.9% accuracy | Non-invasive screening | Still in prototype stage; broader validation needed |
Cui et al.21 | Cell detection | NPANet + Adaptive-IoU | Urine sediment images | SOTA performance | Urinary microscopy automation | Focused on small datasets; not field-tested |
Durán Acevedo et al.24 | Prostate cancer | Electronic tongue + ML | Carbon electrode classification | 92.9% accuracy | Alternative to biopsy | Repetition of previous methods; needs multi-center trials |
Akhtar et al.13 | Urine sediment | VGG-19 CNN | 12-class image classification | 98% accuracy | Smart pathology tools | Dataset bias; real-world robustness untested |
Ye et al.65 | Albumin | UVC LED + quantum dot | Label-free colorimetry | 0.56–0.61 mg/mL | Miniature real-time detection | Focused on limited biomarkers |
Ja’farawy et al.14 | Uric acid | SERS-based aptasensor | AgNPs on PDMS | 0.32 µM; R² = 0.998 | Low-cost UA testing | May be influenced by interfering substances |
AlSayed Ali et al. (2024) | Creatinine | Bifilar helix antenna + ML | EM scattering | 93% accuracy | Instant chemical-free testing | Still under early-stage testing |
Fajardo et al.25 | Dehydration | LIG + EIS + ML | Urine osmolarity | R² ≈ 0.96 | Low-cost hydration monitor | Not yet validated in large clinical trials |
Panda et al.47 | Kidney stones | ML (logistic regression) | Urine chemical features | 93% accuracy | Cost-effective diagnosis | Limited to six urine parameters |
Yang & Deng (2025) | Heart failure risk | GBDT | Heavy metal exposure | AUC = 0.92 | Environmental cardiology insights | Observational data; causality not confirmed |
Siegel et al.58 | Tobacco effects | ML clustering | Urinary biomarkers | HR = 2.24 (high risk) | E-cigarette risk analysis | Requires biomarker standardization |
Mosterd et al.46 | Creatinine | MOF@CDs fluorescent probe | Dual-mode detection | 0.23 µM | Environmental correction | Laboratory-only validation |
Geethukrishnan et al.29 | Uric acid | CoFe-LDH@rGO biosensor | Electrochemical synergy | 0.17 µM | Stable UA sensor | Lacks large-scale clinical trials |
Sheele et al.56 | Antibiotic prediction | XGBoost | Clinical metadata | High accuracy | ED treatment optimization | Model generalizability not evaluated |
