By Pooja Toshniwal PahariaOct 16 2023Reviewed by Lily Ramsey, LLM In a recent study published in Nature, researchers developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system malignancies based on sparsity profiles.
About the study In the present study, researchers designed the Sturgeon machine learning classifier for pediatric and adult CNS tumor categorization, which may be utilized to improve surgical decision-making. Samples were categorized using four submodels during inference, and the scores from the highest confidence level submodel were used for the final classification. The researchers adjusted the sequencing sparsity ranges to guarantee an equal distribution of simulated sequencing times.
Results Sturgeon provided a correct diagnosis in 45 of 50 retrospectively sequenced samples within 40 minutes of commencing sequencing. It was effective in real-time during 25 procedures, with a diagnostic turnaround time of less than 90 minutes. Of these, 18 were right, while seven did not meet the acceptable confidence level.
Sturgeon was the first to transfer the computationally difficult model training, validation, and calibration process outside of the surgical time frame, resulting in well-tested, highly accurate one-size-fits-all classification models.