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. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

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%). In the external testing dataset, 64 eyes were included from the remaining centre. All eyes underwent macular OCT scanning at baseline and 1 month after VILMP. The area under the receiver operated characteristic curve (AUC), accuracy, specificity and sensitivity were used to evaluate the performance of the models. RESULTS: In the external testing set, the AUC, accuracy, specificity and sensitivity of the MH aetiology classification model were 0.965, 0.950, 0.870 and 0.938, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative MH status prediction model were 0.904, 0.825, 0.977 and 0.766, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative idiopathic MH status prediction model were 0.947, 0.875, 0.815 and 0.979, respectively. CONCLUSION: Our DL-based models can accurately classify the MH aetiology and predict the MH status after VILMP. These models would help ophthalmologists in diagnosis and surgical planning of MH.

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. First, 1,792 preoperative macular OCT parameters and 768 clinical variables of 256 eyes from two ophthalmic centers were used to train and internally validate ML models. Second, 224 preoperative macular OCT parameters and 96 clinical variables of 32 eyes from the other two centers were utilized for external validation. To fulfill the purpose of predicting postoperative IMH status (i.e., closed or open), five ML algorithms were trained and internally validated by the ten-fold cross-validation method, while the best-performing algorithm was further tested by an external validation set. RESULTS: In the internal validation, the mean area under the receiver operating characteristic curves (AUCs) of the five ML algorithms were 0.882-0.951. The AUC, accuracy, sensitivity, and specificity of the best-performing algorithm (i.e., random forest, RF) were 0.951, 0.892, 0.973, and 0.904, respectively. In the external validation, the AUC of RF was 0.940, with an accuracy of 0.875, a specificity of 0.875, and a sensitivity of 0.958. CONCLUSIONS: Based on the preoperative OCT parameters and clinical variables, our ML model achieved remarkable accuracy in predicting IMH status after VILMP. Therefore, ML models may help optimize surgical planning for IMH patients in the future.

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. In the external validation set, 72 preoperative macular OCT images of 36 MH eyes treated by VILMP from another two ophthalmic centers were used to validate the prediction accuracy of the DL model. RESULTS: In internal training, the mean of overall accuracy for prediction of MH status after VILMP was 84.6% with a mean area under the receiver operating characteristic (ROC) curve (AUC) of 91.04% (sensitivity 85.37% and specificity 81.99%). In external validation, the overall accuracy of predicting MH status after VILMP was 84.7% with an AUC of 89.32% (sensitivity 83.33% and specificity 87.50%). The heatmaps showed that the area critical for prediction was at the central macula, mainly at the MH and its adjacent retina. CONCLUSIONS: The DL model trained by preoperative macular OCT images can be used to predict postoperative MH status after VILMP. The prediction accuracy of our DL model has been validated by multiple ophthalmic centers.