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Severity prediction in patients with oedema in cerebral contusion using deep learning from computed tomography scans
1Department of Radiology, Daping Hospital, Army Medical University, Chongqing, 400042, China.
Aim:
To develop a model combing deep learning (DL) to evaluate the severity of cerebral contusion oedema.
Materials And Methods:
A DL approach along with the Tada formula and manual delineation were employed to quantify the volume of traumatic intracerebral haemorrhage (n = 141). In a separate cohort of patients with cerebral contusion oedema, participants were divided into high-risk (n = 56) and low-risk (n = 66) subgroups according to the Glasgow outcome scale at 21 days. The volume of haematoma was calculated using the aforementioned DL approach. A model integrating haematoma and oedema was developed, referred to as the oedema index (EI). Marshall computed tomography (CT) classification, A5 (+1) CT classification, oedema volume (EV), and EI were applied to evaluate the severity of oedema.
Results:
The volume estimation of DL approach yielded a volume percentage error of 3.59 % when compared to manual delineation. Regarding predictive efficacy for oedema severity, the area under the curve (AUC) for EV (0.840) was higher than that of the Marshall CT classification (0.664), A5 (+1) CT classification (0.692), and EI (0.731) (all P < .05). Furthermore, the combination of EI and the Marshall CT classification resulted in an AUC increase of 0.081 compared to the Marshall CT classification alone (P = .021), while the combination of EI and the A5 (+1) CT classification led to an AUC increase of 0.075 compared to the A5 (+1) CT classification alone (P = .013).
Conclusion:
A combined model integrating haematoma and oedema using DL techniques can be used to predict the severity of cerebral contusion oedema.
