AI Outperforms Traditional Analysis Methods
Standardisation Issues Traditional Method Problems
End-user variability: Users can change thermocycler software settings leading to inconsistent results. Operator variations: Different technicians produce different outcomes with the same samples. Threshold inconsistency: Manual threshold placement creates run-to-run variations that compromise reproducibility.
False Positives Problems Critical Analysis Failures
Linear rises above threshold: Traditional methods mistake gradual baseline drift for true amplification. High noise samples: Signal artifacts trigger false positive calls in challenging laboratory conditions. Noisy negatives: Background noise exceeds threshold settings, generating incorrect positive results.
False Negatives Challenges Missed Detections
Incorrect CT placement: Poor threshold positioning misses genuine amplification events. Shape differentiation failure: Traditional methods cannot distinguish between different curve morphologies. Manual review dependency: Non-standardized analysis requires expert interpretation, introducing delays and subjective bias.