According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

This disclosure relates generally to system and method for classification of sensitive date using federated semi-supervised learning. Federated learning has emerged as a privacy-preserving technique to learn one or more machine learning (ML) models without requiring users to share their data. In federated learning, data distribution among clients is imbalanced resulting with limited data in some clients. The method includes extracting a training dataset from one or more data sources and pre-processing the training dataset into a machine readable form based on associated data type. Further, a federated semi-supervised learning model is iteratively trained based on a model contrastive and distillation learning to classify sensitive data from the unlabeled dataset. Then, sensitive data from a user query is received as input which are classified using the federated semi-supervised learning model.