Complexity-Pursuit Topography Tracks Spatial-Temporal Severity Structure in Lung Ultrasound Videos.

OBJECTIVE: To evaluate complexity pursuit as an interpretable decomposition of lung ultrasound video dynamics and determine whether pleural-proximal spatial-temporal descriptors track ordinal severity. METHODS: A 46-video public cohort was used for construct validation across COVID-19, normal, bacterial pneumonia and viral pneumonia videos. Complexity-pursuit modes were represented by temporal traces and spatial maps and compared with principal components, random projection and spatial shuffles. After geometry correction and confound screening, four descriptors were retained. Severity validation used a separate, non-overlapping set of 151 COVIDx-US videos with dataset-encoded severity scores from 0 to 3. RESULTS: The fixed pleural-proximal energy fraction and its region-of-interest-normalized counterpart were moderately associated with severity (ρ=-0.576 and -0.572; both q<10-13). Associations remained after source adjustment (ρ=-0.355 and -0.365 ; both q<10-4 ) and preserved direction across all leave-one-source-out analyses. In proportional-odds models, each standard-deviation increase in either descriptor was associated with a lower odds of a higher severity score (odds ratios, 0.26 and 0.27). Adjacent-pair area under the curve point estimates ranged from 0.64 to 0.72, with greater uncertainty for comparisons involving the small score-1 sub-group. The two pleural descriptors were nearly collinear and did not support a combined multivariable score. CONCLUSION: Complexity-pursuit pleural-proximal topography provides a compact and interpretable representation of severity-linked organization in lung ultrasound videos.