Evidence map›Paper›PMID 35038079›Full record

ArticleArchives of osteoporosis2022

Development of an open-source measurement system to assess the areal bone mineral density of the proximal femur from clinical CT images.

Keisuke Uemura, Yoshito Otake, Masaki Takao, Hiroki Makino, Mazen Soufi, Makoto Iwasa, Nobuhiko Sugano, Yoshinobu Sato

Abstract read
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In one paragraph

Article in Archives of osteoporosis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Side-to-side differences in hip bone mineral density and associated morphological factors.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Deep learning in the radiologic diagnosis of osteoporosis: a literature review.The Journal of international medical research · 2024
    Review
  8. Article
  9. Article
  10. Improved distinct bone segmentation in upper-body CT through multi-resolution networks.International journal of computer assisted radiology and surgery · 2023
    Article
  11. Article
  12. Article
  13. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Keisuke UemuraDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan. surmountjp@gmail.com.ORCID http://orcid.org/0000-0002-9245-1743
Yoshito OtakeDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
Masaki TakaoDepartment of Orthopaedics, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Hiroki MakinoDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
Mazen SoufiDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
Makoto IwasaDepartment of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Nobuhiko SuganoDepartment of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Yoshinobu SatoDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
Nara Institute of Science and Technology · JPThe University of Osaka · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Commercial software is generally needed to measure the areal bone mineral density (aBMD) of the proximal femur from clinical computed tomography (CT) images. This study developed and verified an open-source reproducible system to quantify CT-aBMD to screen osteoporosis using clinical CT images. PURPOSE: For existing CT images acquired for various reasons other than osteoporosis, it might be beneficial to estimate areal BMD as assessed by dual-energy X-ray absorptiometry (DXA-based BMD) to ascertain the bone status based on DXA. In this study, we aimed to (1) develop an open-source reproducible measurement system to quantify DXA-based BMD from CT images and (2) validate its accuracy.

methodsThis study analyzed 75 pairs of hip CT and DXA images of women that were acquired for the preoperative assessment of total hip arthroplasty. From the CT images, the femur and a calibration phantom were automatically segmented using pre-trained codes/models available at https://github.com/keisuke-uemura . The proximal femoral region was isolated by manually selected landmarks and was projected onto the coronal plane to measure the areal density (CT-aHU). The calibration phantom was employed to convert the CT-aHU into CT-aBMD. Each parameter was correlated with DXA-based BMD, and the residual errors of CT images to estimate the T-scores in DXA were calculated using the standard error of estimate (SEE).

resultsThe correlation coefficients of DXA-based BMD with CT-aHU and CT-aBMD were 0.947 and 0.950, respectively (both p < 0.001). The SEE for quantifying the T-scores in DXA were 0.51 and 0.50 for CT-aHU and CT-aBMD, respectively.

conclusionWith the method developed herein, CT permits estimation of the DXA-based BMD of the proximal femur within the standard DXA total hip region of interest with an SEE of 0.5 in T-scores. The radiation dose for CT acquisition needs consideration; therefore, our data do not provide a rationale for performing CT for screening osteoporosis. However, on CT images already acquired for clinical indications other than osteoporosis, researchers may use this open-source system to investigate osteoporosis status through the estimated DXA-based BMD of the proximal femur.

Indexed as

Bone DensityTomography, X-Ray ComputedAbsorptiometry, PhotonFemaleFemurHumansReproducibility of ResultsArtificial intelligenceConvolutional neural network (CNN)Deep learningDual-energy X-ray absorptiometryOpen-source systemQuantitative computed tomography

Identifiers

PMID35038079
OpenAlexW4206273597

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.