In comparison with MRI, the present ultrasonic approach may allow real-time monitoring of depth-dependent osmotic behaviours by the echo shift and the changes in amplitude.
Although future implementations of this system may allow real-time alerts to inform management, consistent experience to date with existing commercial telehealth systems has been that the high rates of false-positive alerts have been unacceptable to clinicians and patients.
Biomarkers may also allow real-time testing to monitor whether disease progression has been changed by therapeutic interventions [13].
In the future, the nascent text-to-speech and animation technologies may allow for real-time artificial expressions of agent emotional cues, via immediate controls of voice tones, facial expressions, and animation gestures of a pedagogical agent.
Wireless medical devices may allow in situ, real-time biomedical monitoring and detection [11, 12].
This may allow high-quality, real-time, international training of diagnostic and therapeutic endoscopy techniques at a low cost.
As shown in numerous studies, circulating miRs can serve as predictors of cancer outcome [18 22] and may allow for a real-time assessment of treatment responses after surgical resection [23], chemotherapy [24 27] or pathway targeted therapy [28].
While previous prenatal studies focused on foetal DWI with relatively high b-factors (e.g. 500 800 s/mm2) [7, 15, 16, 17, 18, 19], we assume that additional lower b-factors may allow the separation of real diffusion and perfusion effects, improving the specificity for pathological changes in microvascular perfusion of the foetal organs in utero.
Therefore, the authors believe the design of the present study, with a true placebo intervention (i.e., the patient does not know that the ultrasound is ineffective), may allow to isolate the real effect of manual therapy.
To address this challenge, technology may allow a playback feature or provide real-time metrics that allow learners to observe what their body is doing with respect to the important features of the target learning domain.
