Correlating Age and Hematoma Volume with Extent of Midline Shift in Acute Subdural Hematoma Patients: Validation of an Artificial Intelligence Tool for Volumetric Analysis.

OBJECTIVE: Decision for intervention in acute subdural hematoma patients is based on a combination of clinical and radiographic factors. Age has been suggested as a factor to be strongly considered when interpreting MLS and hematoma volume data for assessing critical clinical severity during operative intervention decisions for acute subdural hematoma patients. The objective of this study was to demonstrate the use of an automated volumetric analysis tool to measure hematoma volume and midline shift (MLS) and quantify their relationship with age.

METHODS: 1789 acute subdural hematoma patients were analyzed using qER-Quant software (Qure.ai, Mumbai, India) for MLS and hematoma volume measurements. Univariable and multivariable regressions analyzed association between MLS, hematoma volume, age, and MLS-hematoma volume ratio.

RESULTS: In comparison to younger patients (≤70 years), older patients (>70 years) had significantly higher average hematoma volume (old: 62.2 mL vs. young: 46.8 mL, p<0.0001), lower average MLS (old: 6.6 mm vs. young: 7.4 mm, p=0.025), and lower average MLS-hematoma volume ratio (old: 0.11 mm/mL vs. young 0.15 mm/mL, p<0.0001). Young patients had an average of 1.5mm greater MLS for a given hematoma volume in comparison to older patients. With increasing age, the ratio between MLS and hematoma volume significantly decreases (p=0.0002).

CONCLUSION: Commercially-available, automated, AI-based tools may be used for obtaining quantitative radiographic measurement data in patients with acute subdural hematoma. Our quantitative results are consistent with the qualitative relationship previously established between age, hematoma volume, and MLS, which supports the validity of using AI-based tools for acute subdural hematoma volume estimation.