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Related Concept Videos

Nursing Diagnosis01:22

Nursing Diagnosis

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Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
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Formulating and Validating Nursing Diagnosis I01:26

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A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Muscles of the Eye01:20

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The muscles of the eye are sophisticated structures that control eye movement and focus, allowing for the precise and rapid adjustments necessary for vision. The human eye is controlled by ten muscles — six extraocular muscles, three intraocular muscles, and one primary eyelid retractor muscle.
Extraocular Muscles
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Formulating and Validating Nursing Diagnosis II01:25

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Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
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Diabetes: Symptoms, Diagnosis, and Complications01:15

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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Eye-tracking-aided digital system for strabismus diagnosis.

Zeng Hai Chen1, Hong Fu1, Wai Lun Lo1

  • 1Department of Computer Science, Chu Hai College of Higher Education, Tsuen Wan, Hong Kong.

Healthcare Technology Letters
|March 9, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an objective, digital eye-tracking system for diagnosing strabismus, a common childhood vision disorder. The system analyzes gaze data to provide accurate and convenient strabismus detection, improving upon subjective traditional methods.

Keywords:
amblyopiaeye gazeeye-tracking-aided digital systemgaze trackingpatient diagnosispermanent vision losspreschool childrenstrabismus diagnosisvision defectsvision disorders

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Computer Vision

Background:

  • Strabismus is a prevalent vision disorder in children, potentially leading to amblyopia and vision loss.
  • Traditional strabismus diagnosis relies on subjective ophthalmologist experience, posing challenges for large-scale screening.
  • Negative impacts of strabismus extend beyond vision, affecting daily life, education, and employment.

Purpose of the Study:

  • To develop an objective, digital, and automatic system for strabismus diagnosis.
  • To leverage eye-tracking technology for precise gaze data acquisition.
  • To establish an effective method for strabismus screening in community settings.

Main Methods:

  • An eye-tracking system was developed to capture eye gaze data while participants viewed targets.
  • Novel features were engineered to characterize the acquired gaze data.
  • A dedicated strabismus gaze dataset was created using the developed system.

Main Results:

  • The proposed system demonstrated effectiveness in diagnosing strabismus based on gaze features.
  • Experimental results validated the system's performance on the created dataset.
  • The system offers an objective and automated approach to strabismus detection.

Conclusions:

  • The developed eye-tracking system provides an objective and digital method for strabismus diagnosis.
  • This technology can overcome the limitations of subjective and inconvenient traditional diagnostic approaches.
  • The system shows promise for efficient and accurate strabismus screening in community and educational settings.