A mathematical and computational framework to predict the time to and recovery from osteoporosis via serial DEXA scans: a proof-of-concept model for digital decision support

P. Scully, Azrin Muslim, T. Sheehy, A. Costelloe, N. Asri, D. Lyons · 2026

Read A mathematical and computational framework to predict the time to and recovery from osteoporosis via serial DEXA scans: a proof-of-concept model for digital decision support by P. Scully, Azrin Muslim, T. Sheehy, A. Costelloe, N. Asri, D. Lyons (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13348139.txt.

Description

Scully and co-authors build a math model that predicts when a patient's bones will cross into osteoporosis in a 2026 paper. Bone density scans are the standard diagnostic, and patients often have several across years. Yet no published model turned those repeat scans into an explicit age prediction for onset or recovery.

Their framework uses two methods: a simple slope from two scans and a fitted line across many. It was tested on two hundred patients with varied scan counts. Only a minority crossed into osteoporosis during follow-up, which limited onset checks. The team also tested how well earlier scans forecast the next one and how prediction ranges narrow as scans accumulate. The proof of concept targets digital decision support.

Catalog record

CREATORP. Scully, Azrin Muslim, T. Sheehy, A. Costelloe, N. Asri, D. Lyons
YEAR2026
COLLECTIONPAPERS
FORMATTXT
FILE SIZE43 KB
VERSION1.0
LICENSECC BY 4.0
RIGHTSCC BY
SOURCEEurope PMC

Source: https://europepmc.org/articles/PMC13348139

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