AI Outperforms Traditional Analysis Methods

Traditional Methods Create Operator Dependency

Traditional PCR analysis suffers from fundamental standardisation problems that compromise reproducibility. End-users can change thermocycler software settings, leading to variations between runs and operators. Manual threshold placement introduces subjective bias, while inconsistent baseline settings create run-to-run variations even with identical samples.

THE PCR.AI API HAS ZERO SETTINGS PER RUN - ONCE TRAINED (PER TARGET), RESULTS ARE 100% REPEATABLE.

Traditional Methods Cannot Handle Artifacts

Curves can exhibit dramatic noise spikes that cross threshold lines, creating false positive calls. These artifacts result from chemistry problems, instrument malfunctions, or environmental factors. Traditional threshold-based systems cannot distinguish between genuine amplification signals and these transient noise events. The AI recognizes spike patterns and filters them as artifacts rather than true amplification.

Not All 'Late CTs' are Identical

Curves with similar CTs can have vastly different shapes that conventional methods cannot differentiate. Traditional threshold analysis treats all curves crossing at the same cycle as equivalent, regardless of curve morphology. However, identical CT values can represent genuine exponential amplification, inhibited reactions, or false positive artifacts. The shape of the curve reveals the true nature of the amplification event that CT alone cannot distinguish.