A team of international researchers has developed a deep learning system capable of reliably detecting and quantifying lesions associated with traumatic brain injury from patients’ CT scans. The system, outlined May 14 in The Lancet: Digital Health, utilizes data from multiple institutions across Europe and was validated using scans from more than 500 patients in India. Compared with manual assessment, the CNN produced similarly accurate measurements, allowing clinicians to quantify lesion burden and progression.
The researchers trained and validated their convolutional neural network on manual segmentations performed in 98 scans. That algorithm was then used to segment a new dataset of scans which comprised 839 CTs from 38 institutions. Their model accurately segmented, quantified, and detected multiclass hemorrhagic lesions and perilesional oedema. Specifically, these measurements can aid in image-based diagnoses, determine brain injury type, quantify severity, and gauge injury progression