Machine Learning for CRISPR-Based Diagnostics

CRISPR-based diagnostics now detect viral, bacterial, and cancer-associated nucleic acids with sensitivities approaching quantitative PCR; however, their translation to decentralized care rests on computational design and interpretation that current datasets cannot sustain. Pandemic-era Cas12a assays reached 95% positive predictive agreement against reverse transcription quantitative PCR (RT-qPCR) at 10 copies/μL, and deep neural networks now design Cas13 detection assays spanning 1933 vertebrate-infecting viruses, ranking candidate guides at Spearman correlations of 0.69 to 0.84 across internal and external validation. Generative deep-learning systems improve single-nucleotide discrimination two- to three-fold, computer vision classifies lateral flow outputs at 96.5% accuracy, and multi-biomarker fusion reaches an area under the receiver operating characteristic curve (AUC) of 0.998 in lung cancer detection. These results mask a narrow data foundation. Cas13a guide prediction still draws from a single screening library of 19,209 guide–target pairs, Cas12a has one published diagnostic model, and signal classifiers almost uniformly validate on single-site cohorts. This review synthesizes mechanistic constraints, predictive and generative models, and point-of-care classifiers, and maps the path beyond this data ceiling. Evolutionary pretraining on RNA corpora and lab-in-the-loop agents that convert model failure into targeted data acquisition define the route forward.