Konica Minolta Healthcare is highlighting a recent study that used the company's technology to develop a machine learning-based analysis of x-ray imaging that automatically quantifies lung function data.
The study, published in Chest Pulmonary and led by researchers at the Icahn School of Medicine at Mount Sinai, suggests that the AI-powered technique can serve as an alternative to pulmonary function tests. It used dynamic digital radiography (DDR) with the AI analysis to provide additional quantitative data that could differentiate pulmonary disorders and serve as a possible marker for treatment escalation.
The researchers created an analysis pipeline using a convolutional neural network (CNN) to quantify DDR data in lung areas during normal respiration, maximal inhalations, and exhalations. They compared pulmonary function tests to the DDR-based pulmonary function tests across multiple data points and found a strong correlation between the two technologies. The team also found that the automated DDR pipeline provides lung area-time and flow-area loops analogous to test volume-time and flow-volume loops.
The researchers noted that DDR can depict diaphragm motion and respiratory muscle synchrony and could serve as a screening tool for pulmonary physiology workup and as a treatment aid. The AI-based software developed by the authors for the study is available as an open-source code on GitHub.
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![Representative example of a 16-year-old male patient with underlying X-linked adrenoleukodystrophy. (A, B) Paired anteroposterior (AP) chest radiograph and dual-energy x-ray absorptiometry (DXA) report shows lumbar spine (L1 through L4) areal bone mineral density (BMD). The DXA report was reformatted for anonymization and improved readability. The patient had low BMD (Z score ≤ −2.0). (C) Model (chest radiography [CXR]–BMD) output shows the predicted raw BMD and Z score in comparison with the DXA reference standard, together with interpretability analyses using Shapley additive explanations (SHAP) and gradient-weighted class activation maps. The patient was classified as having low BMD, consistent with the reference standard. AM = age-matched, DEXA = dual-energy x-ray absorptiometry, RM2 = room 2, SNUH = Seoul National University Hospital, YA = young adult.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/ai-children-bone-density.0snnf2EJjr.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
