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Radiomorphometric Parameters in Mandibular Panoramic Radiographs of Hypothyroid Patients: A Cross-Sectional Study.

Ananya Nair1, Vathsala Patil1, Isha Bhardwaj1

  • 1Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

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Summary

Hypothyroidism can affect bone density, increasing fracture risk. Radiomorphometric parameters on dental panoramic images, particularly the antegonial index (AGI), may indicate these bone changes in hypothyroid patients.

Keywords:
bone mineral densityhypothyroidismosteoporosispanoramic radiographsradiomorphometric indices

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Area of Science:

  • Oral and Maxillofacial Radiology
  • Endocrinology
  • Bone Metabolism

Background:

  • Thyroid hormones influence bone mineral homeostasis, with hypothyroidism linked to reduced bone mineral density and increased osteoporosis risk.
  • Radiomorphometric parameters derived from panoramic imaging serve as indicators of bone mineral density.

Purpose of the Study:

  • To compare radiomorphometric parameters in dental panoramic images of patients with hypothyroidism against a matched control group.
  • To assess the utility of mandibular radiomorphometric indices in identifying bone changes associated with hypothyroidism.

Main Methods:

  • Panoramic radiographs from 47 hypothyroidism patients and 50 healthy controls were analyzed.
  • Key radiomorphometric indices measured included mental index (MI), inferior panoramic mandibular index (PMI), antegonial index (AGI), gonial index (GI), and mandibular cortical index (MCI).

Main Results:

  • Mean MI, PMI, and GI values were lower in the hypothyroidism group compared to controls.
  • The antegonial index (AGI) demonstrated a statistically significant difference between the hypothyroidism and control groups (p = 0.03).

Conclusions:

  • Radiomorphometric parameters like MI, PMI, GI, and AGI are potential indicators of mandibular bone changes in systemic diseases such as hypothyroidism.
  • While AGI showed statistical significance, further research with larger cohorts and advanced techniques like machine learning is recommended to validate these findings and explore predictive capabilities for bone mineral density.