Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

Agarwal, Siddharth; Wood, David A.; Modat, Marc; Booth, Thomas C.

doi:10.1136/jnis-2023-020218

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…However, realising this goal will involve overcoming several challenges. One challenge is the lack of representativeness of research datasets (Agarwal et al, 2023; Agarwal & Wood et al, 2023; Din et al, 2023), particularly public datasets commonly used for training brain age models. This applies not only to the demographics of the study participants, but also to the nature of the MRI data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting

Wood,

Townend,

Guilhem

et al. 2024

Human Brain Mapping

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Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2‐weighted, T2‐FLAIR, T1‐weighted, diffusion‐weighted, and gradient‐recalled echo T2*‐weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine‐tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.

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Section: Introductionmentioning

confidence: 99%