Artificial intelligence (AI) and machine learning describe a broad range of algorithm types
that can be trained based on datasets to make predictions. The increasing sophistication of …

Purpose With the in-depth application of machine learning (ML) in clinical practice, it has
been used to predict the mortality risk in patients with traumatic brain injuries (TBI). However …

Although a substantial amount of studies is dedicated to morph detection, most of them fail to
generalize for morph faces outside of their training paradigm. Moreover, recent morph …

Background Accurate patient-specific predictions on return-to-work after traumatic brain
injury (TBI) can support both clinical practice and policymaking. The use of machine learning …

With the rise of precision and personalized medicine for support of medical decisions,
machine learning is gaining popularity in the field of intensive care, first not only to predict …

Defining reliable tools for early prediction of outcome is the main target for physicians to
guide care decisions in patients with brain injury. The application of machine learning (ML) …

There has been an explosion of research into biofluid (blood, cerebrospinal fluid, CSF)-
based protein biomarkers in traumatic brain injury (TBI) over the past decade. The …

In this paper, we seek to draw connections between the frontal and profile face images in an
abstract embedding space. We exploit this connection using a coupled-encoder network to …

Purpose We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging
data as input to predict various outcomes in traumatic brain injury (TBI) patients. Methods In …

In this narrative review, we explore the evolving role of machine learning (ML) in the
diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The …