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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Classification of Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

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Related Experiment Videos

A deep learning-based classification method for subclinical zonular laxity in AS-OCT images.

Jialin Liu1, Lujie Zhang2,3, Shuaixin Lu2

  • 1School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China.

Frontiers in Cell and Developmental Biology
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method for detecting and locating subclinical zonular laxity in anterior segment optical coherence tomography (AS-OCT) images. The AI tool shows promise for improving cataract surgery planning and patient safety.

Keywords:
anterior segment optical coherence tomographycataract surgerydeep learninghuman lens zonularmedical image classificationpreoperative assessment

Related Experiment Videos

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Subclinical zonular laxity can complicate cataract surgery.
  • Accurate detection and localization of zonular abnormalities are crucial for surgical planning.

Purpose of the Study:

  • To develop and validate a deep learning (DL) method for detecting and identifying the angular position of subclinical zonular laxity.
  • To assess the clinical applicability of the DL method as a preoperative screening tool.

Main Methods:

  • A dataset of 600 anterior segment optical coherence tomography (AS-OCT) images was curated and divided into subclinical zonular laxity and normal control groups.
  • A novel DL framework, MDCL-Net, was implemented, incorporating mask-aware feature enhancement and dynamic contextual feature aggregation.
  • Data partitioning at the patient level ensured robust validation and testing, with additional external validation on five clinical cases.

Main Results:

  • The DL model achieved an accuracy of 79.72%, an AUC of 86.41%, and an F1-score of 78.93%.
  • Ablation studies validated the contribution of individual model modules.
  • Clinical validation demonstrated good agreement between model predictions and intraoperative observations.

Conclusions:

  • This study presents the first DL method for simultaneous detection and spatial localization of subclinical zonular abnormalities in AS-OCT images.
  • The developed method shows strong clinical applicability and potential as a reliable preoperative screening tool.
  • The tool can enhance surgical planning and safety in cataract procedures.