Precise detection of awareness in disorders of consciousness using deep learning framework.

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC.

Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole.

Aims: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peeling (VILMP).

Methods: In this multicentre retrospective cohort study, a total of 330 MH eyes with 1082 optical coherence tomography (OCT) images and 3300 clinical data enrolled from four ophthalmic centres were used to train, validate and externally test the DL and MDFN models. 266 eyes from three centres were randomly split by eye-level into a training set (80%) and a validation set (20%).

Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery.

Background: To develop a machine learning (ML) model for the prediction of the idiopathic macular hole (IMH) status at 1 month after vitrectomy and internal limiting membrane peeling (VILMP) surgery.

Methods: A total of 288 IMH eyes from four ophthalmic centers were enrolled. All eyes underwent optical coherence tomography (OCT) examinations upon admission and one month after VILMP.

A multi-center study of prediction of macular hole status after vitrectomy and internal limiting membrane peeling by a deep learning model.

Background: To develop a deep learning (DL) model for prediction of idiopathic macular hole (MH) status after vitrectomy and internal limiting membrane peeling (VILMP) based on optical coherence tomography (OCT) images from four ophthalmic centers.

Methods: Eyes followed up at 1 month after VILMP for full-thickness MH were included. In the internal training set, 920 preoperative macular OCT images (as the input) and post-operative status of MH (closed or open, as the output) of 256 eyes from two ophthalmic centers were used to train the DL model using VGG16 algorithm.