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Related Concept Videos

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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A New Insight into Imaging Diagnosis of Otosclerosis Enhanced by Machine Learning and Radiomics.

Marta Álvarez de Linera-Alperi1, Juan Miranda Bautista2,3, David Corral Fontecha4

  • 1Otorrinolaringology Department, Hospital Universitario Rey Juan Carlos, Madrid, Spain.

Journal of Imaging Informatics in Medicine
|January 30, 2026
PubMed
Summary

Radiomics and machine learning (ML) enhance otosclerosis diagnosis using CT scans. This approach improves accuracy and reduces false negatives, aiding in better patient care and surgical planning for this ear disease.

Keywords:
Imaging biomarkersMachine learningOtosclerosisRadiomicsTemporal bone computed tomography

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Area of Science:

  • Medical Imaging
  • Radiology
  • Otolaryngology

Background:

  • Otosclerosis causes hearing loss via abnormal bone remodeling and stapes fixation.
  • High-resolution computed tomography (HRCT) is standard but has a high false-negative rate.
  • Improved diagnostic accuracy is needed for otosclerosis.

Purpose of the Study:

  • To investigate radiomics and machine learning (ML) for improved otosclerosis diagnosis.
  • To assess the diagnostic performance of ML models on HRCT data.
  • To identify imaging biomarkers for otosclerosis.

Main Methods:

  • Radiomic feature extraction (6048 features) from HRCT scans of 99 subjects (48 otosclerosis, 51 controls).
  • Feature selection reduced features to 1317; statistical analysis and ML modeling were applied.
  • L2-regularized logistic regression was used as the primary ML classifier.

Main Results:

  • Sixty-seven significant biomarkers were identified, mainly in the antefenestral region and stapes, indicating increased heterogeneity.
  • ML model achieved an Area Under the Curve (AUC) of 0.90 ± 0.06, outperforming standard radiological diagnosis.
  • Image transformation filters improved visualization of disease-related changes.

Conclusions:

  • Radiomics and ML show significant potential to enhance otosclerosis diagnostic accuracy and standardize the process.
  • This approach can help reduce false-negative rates in HRCT imaging.
  • Findings support the use of radiomics and ML for better surgical decision-making in otosclerosis.