Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Documentation IV: Focus Charting01:26

Methods of Documentation IV: Focus Charting

1.1K
Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
1.1K
Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

1.5K
Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
1.5K
Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

3
Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
3
Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

1.1K
The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

COSMICA: A Novel Dataset for Astronomical Object Detection with Evaluation Across Diverse Detection Architectures.

Journal of imaging·2025
Same author

Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet.

Sensors (Basel, Switzerland)·2022
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
See all related articles

Related Experiment Video

Updated: Sep 21, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
05:58

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

4.7K

Digital Hebrew Paleography: Script Types and Modes.

Ahmad Droby1, Irina Rabaev2, Daria Vasyutinsky Shapira1

  • 1Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva 8410501, Israel.

Journal of Imaging
|May 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning tool to automatically classify medieval Hebrew manuscripts by script style and mode. The AI achieves human-level accuracy, significantly speeding up manuscript classification for researchers.

Keywords:
Hebrew medieval manuscriptsconvolutional neural networkdeep-learning based classificationdigital paleographyhandwritten style analysisscript type classification

More Related Videos

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities
10:14

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Published on: October 25, 2024

3.9K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

15.0K

Related Experiment Videos

Last Updated: Sep 21, 2025

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
05:58

Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment

Published on: March 11, 2021

4.7K
Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities
10:14

Author Spotlight: Leaf Trait Analysis for Climate and Ecology Reconstruction in Modern and Ancient Plant Communities

Published on: October 25, 2024

3.9K
Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis
05:52

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Naïve Adolescents at High Risk for Psychosis

Published on: November 21, 2013

15.0K

Area of Science:

  • Digital Humanities
  • Computational Paleography
  • Artificial Intelligence in Historical Studies

Background:

  • Paleography, the study of ancient handwriting, is crucial for historical text analysis.
  • Numerous medieval manuscripts remain unclassified due to the limitations of manual expert analysis.
  • Automated tools are needed to efficiently classify large manuscript collections.

Purpose of the Study:

  • To develop and evaluate a deep learning model for classifying medieval Hebrew manuscripts.
  • To classify manuscripts into 14 distinct classes based on script style and graphical mode.
  • To compare the performance of automated classification with human expert paleographers.

Main Methods:

  • Utilized a deep learning methodology for script type classification.
  • Experimented with various input image representations and network architectures.
  • Implemented a hierarchical classification approach: first regional style, then graphical mode.
  • Explored the use of soft labels and a 'squareness value' to refine graphical mode classification.

Main Results:

  • Achieved highest accuracy with the hierarchical classification approach.
  • Redefining graphical mode labels using the 'squareness value' significantly improved classification accuracy.
  • The developed deep learning model demonstrated performance on par with human expert paleographers.

Conclusions:

  • Deep learning offers an effective solution for automated classification of medieval Hebrew manuscripts.
  • Hierarchical classification and refined labeling strategies enhance paleographic analysis accuracy.
  • AI-powered tools can significantly aid researchers in processing and understanding historical textual data.