Posts Tagged Neurological Examination

[VIDEO] Upper Limb Neurological Examination – OSCE Guide (New Version) – YouTube

This video aims to give you an idea of what’s required in the Upper Limb Neurological Examination OSCE. Check out the Geeky Medics quiz platform, with over 700 free medical MCQs:

via Upper Limb Neurological Examination – OSCE Guide (New Version) – YouTube

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[VIDEO] 3 minute neurological examination – YouTube

An excellent neurological examination for General Practice where the purpose is to exclude serious pathology in headaches,

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[ARTICLE] Identifying and Quantifying Neurological Disability via Smartphone – Full Text

Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.


Neurological examination measures diverse functions of the central (CNS) and peripheral nervous systems to diagnose neurological diseases and guide treatment decisions. Thorough neurological examination takes between 30 and 60 min to complete and years of training to master. This poses problem both for developing countries, which often lack neurologists, and for developed countries where cost-hikes and administrative requirements severely limit the time clinicians spend examining patients.

Additionally, clinical scales derived from traditional neurological examination are rather insensitive and prone to biases, which limits their utility in drug development. Therefore, non-clinician administered measurements of physical disability such as timed 25-foot walk (25FW) and 9-hole peg test (9HPT) or measurements of cognitive functions exemplified by paced auditory serial addition test (PASAT) and symbol digit modalities test (SDMT), are frequently used in clinical trials of neurological diseases such as multiple sclerosis (MS) (12). Especially combining these “functional scales” with clinician-based disability scales such as Expanded Disability Status Scale (EDSS)(3) into EDSS-plus (4) or Combinatorial weight-adjusted disability scale (CombiWISE) (5) enhances sensitivity of clinical trial outcomes. However, these sensitive combinatorial scales are rarely, if ever acquired in clinical practice due to time and expense constrains.

Measuring neurological functions by patients via smartphones (68) may pose a solution for all aforementioned problems, while additionally empowering patients for greater participation in their neurological care. We have previously found comparable sensitivity and specificity of simple, smartphone-amenable measurements of finger and foot taps to 9HPT and 25FW, respectively (9). In this study, we explored iterative development/optimization of smartphone-based measurements of neurological functions by: 1. Exploring clinical utility of new features that can be extracted from finger tapping; 2. Development of “balloon popping” smartphone test that builds on finger tapping by expanding neurological functions necessary for task completion to eye movements and cognitive skills, and 3. By decoding app-collected raw data into secondary (derived) features that may better reflect deficits in specific neurological functions.


Materials and Methods

Developing the Smartphone Apps

Tapping and Balloon popping tests were written using Java in the Android Studio integrated development environment. Both tests went through iterative development and optimization following beta testing with developers and then clinical trial testing with patients and healthy volunteers. Each of the individual tests are standalone applications and can be downloaded individually to the phone using an Android Package (APK) emailed to phones or directly installed through USB connection with Android Studio. Installation and initial testing of applications were completed on a variety of personal Android phones, with no particular specifications. Testing in the clinic with patients and longitudinal testing was completed on Google Pixel XL 2017 phones. Android 8.1 Oreo operating system was used for the most recent version of the application, with the intention of keeping the operating system the app runs on up to date with the most recent version released by Android.

For the purposes of this study, we created a front-end application that can flexibly incorporate a variety of test apps. The front-end prompts for user profiles where a testing ID, birth month and year, gender, and dominant hand may be entered so data collected is associated with the user profile. Through a cloud-based spreadsheet, “prescriptions” of test app configurations are set for each user such that they may have a unique combination of tests tailored to their disability level.

The tapping test goal was similar to previously validated non-smartphone administered tapping tests (9), where users had to tap as quickly as possible over a 10 s duration and the final score is the average of two attempts. The test uses touch recognition over a rectangular area covering the bottom half of a vertically oriented phone screen (Figure 1A). Users can tap anywhere in a marked off gray area. The total number of taps for each of two trials and the calculated average is displayed immediately afterwards on the screen. In addition to total taps over the duration of the test, the app also records the duration, Android system time, and pressure for each tap as background data. Pressure for app recording is interpreted from the size of the touch area on each tap, where larger tap area corresponds to a higher pressure reading. Because the pressure function was added later and therefore the data are missing for the majority of current cohort, this function is not investigated in current study.

www.frontiersin.orgFigure 1. Smartphone Apps. (A) Tapping Test where user can tap repeatedly anywhere in the gray rectangle over the bottom half of the screen. (B) Popping Test where the dark blue circle will disappear and simultaneously reappear randomly across the screen as soon as the user touches it.

The balloon popping test was conceptually envisioned as an extension of tapping test that expands neurological functions necessary for test completion from pure motoric, to motoric, visual, and cognitive (attention and reaction time). The primary goal for this test is to touch as many randomly generated dark blue circles (balloons) moving across the screen in succession over the 26-s test duration as possible. During optimization of the app we tested 3 sizes of the target balloon and a 100-pixel balloon was selected as optimal based on preliminary results. The analyses of the other two circle sizes are provided as part of sensitivity analyses (Supplementary Figure 1), as conclusions from these tests support data presented in the main text of the paper. There is only one balloon to pop on the screen at a time (Figure 1B) and as soon as the user touches anywhere on the circle, another circle will appear in a random location. The random generation of balloon locations was created by random number functions in Java for both the x and y coordinates of the center of the circle, with the constraint of the entire balloon having to be visible on the screen. If the user taps on a background location, the current balloon stays in the same location and is only moved to a new random location after accurately tapping on the balloon. Following app completion, the total number of balloons popped and calculated average (from two trials) is displayed on the phone for the user. The x and y coordinates of all balloon and background hits, the system time, duration, and pressure (in the same manner as tap pressure) for each tap are also recorded as background data and stored in cloud-based data system.

Following the completion of a tapping or balloon popping test trial, an intermediate message displayed on the screen asks if the users would like to submit their results or retake the most recent trial (Supplementary Videos 12). If the user selects the retake option the collected data for the trial is discarded locally on the phone and not sent to any cloud-based database. This was implemented to avoid noise associated with test interruptions or other unforeseen circumstances that affected test performance. Following selection of the submit option, the data is uploaded immediately to a cloud-based database if the smartphone is connected to WiFi. If the phone is not connected to WiFi, then the submitted test trial results are stored locally on the phone and uploaded to the database as soon as the phone is connected to WiFi.

The app development process is in continuation given user and clinician feedback in addition to integration of more tests into the front-end. User feedback, user’s ability to perform Apps in a “practice mode”, and training videos for individual tests (Supplementary Videos 12) are integrated into the front-end dashboard that manages different tests.[…]

Continue —->  Frontiers | Identifying and Quantifying Neurological Disability via Smartphone | Neurology

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[VIDEO] Lower Limb Neurological Examination – OSCE guide (New Version) – YouTube

Δημοσιεύτηκε στις 16 Φεβ 2015
See the written guide alongside the video on our website…

This video aims to give you an idea of what’s required in the Lower Limb Neurological Examination OSCE.

Always adhere to your medical schools / local hospital trusts guidelines when performing examinations or clinical procedures. Do NOT perform any examination or procedure on patients based purely upon the content of these videos. Geeky Medics accepts no liability for loss of any kind incurred as a result of reliance upon information provided in this video.

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[VIDEO] Upper Limb Neurological Examination – OSCE Guide (New Version) – YouTube

Δημοσιεύτηκε στις 16 Φεβ 2015
To see the written guide alongside the video head over to our website…

This video aims to give you an idea of what’s required in the Upper Limb Neurological Examination OSCE.

Always adhere to your medical schools / local hospital trusts guidelines when performing examinations or clinical procedures. Do NOT perform any examination or procedure on patients based purely upon the content of these videos. Geeky Medics accepts no liability for loss of any kind incurred as a result of reliance upon information provided in this video.

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