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<p>We present a dataset of terahertz time-domain spectral images of the feet of 80 individuals with type 2 diabetes and 98 non-diabetic subjects. The dataset includes demographic information such as age, sex, weight, height, smoking status, and body mass index. For diabetic participants, the results of the Semmes-Weinstein monofilament test used to assess peripheral neuropathy are also provided. To our knowledge, this dataset represents the largest collection of <i>in vivo</i> cutaneous terahertz reflection spectra from human subjects published to date. The data are made available to support independent analysis and may enable further investigation of the dielectric properties and structure of human skin, as well as potential effects of diabetes. Scripts for loading and visualizing the data are provided in both Matlab and Python. The dataset and accompanying scripts are publicly available at <a href="https://doi.org/10.6084/m9.figshare.31052176">https://doi.org/10.6084/m9.figshare.31052176</a>.</p>

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Terahertz Time-Domain imaging of feet of diabetic and non-diabetic patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Goretti G. Hernandez-Cardoso,
  • Monica Ortiz-Martinez,
  • Blanca O. Murillo-Ortiz,
  • Alfredo Campos Mejia,
  • Lauro F. Amador-Medina,
  • Gerardo Gutierrez-Torres,
  • Edgar S. Reyes-Reyes,
  • Irving Salas-Gutierrez,
  • Enrique Castro-Camus

摘要

We present a dataset of terahertz time-domain spectral images of the feet of 80 individuals with type 2 diabetes and 98 non-diabetic subjects. The dataset includes demographic information such as age, sex, weight, height, smoking status, and body mass index. For diabetic participants, the results of the Semmes-Weinstein monofilament test used to assess peripheral neuropathy are also provided. To our knowledge, this dataset represents the largest collection of in vivo cutaneous terahertz reflection spectra from human subjects published to date. The data are made available to support independent analysis and may enable further investigation of the dielectric properties and structure of human skin, as well as potential effects of diabetes. Scripts for loading and visualizing the data are provided in both Matlab and Python. The dataset and accompanying scripts are publicly available at https://doi.org/10.6084/m9.figshare.31052176.