Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population

Abstract

An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis.

Machine Learning Solutions for Osteoporosis—A Review

“…( 33,63,65 ) Several studies did not report characteristics of their data set ( 48,55,56,59–64 ) or the model selection process. ( 33,39,41,42,45,46,50,51,53,56–61,64,65 ) Performance was significantly impacted by case prevalence where accuracy dropped from 94.0% to 88.4% when tested on 13% and 50% positive (osteoporotic) cases, respectively. ( 44 ) An image enhancement and standardization step, and combining multiple features, was able to considerably improve results in two studies, respectively.…”

“…( 29,32–34,36–65 ) Osteoporosis classification was made based on lumbar BMD, ( 32–34,37,51 ) hip BMD, ( 38,50,58 ) lumbar and hip BMD, ( 29,39–42,46–48,53,59,60 ) other non‐standard assessments, ( 43,44,49,54–56,65 ) or unspecified. ( 36,45,52,57,61–64 ) Studies identified osteoporosis based on opportunistic imaging from CT, ( 32–34 ) X‐ray, ( 37,38,43–45,55–59,63,64 ) or dental imaging; (36,47–49,53,54,60,62 ) other studies used data from patient characteristics, ( 40,41,50,51,61,65 ) bone biomarkers, (29,39 ) or acoustical responses. ( 42,52 ) As outcome, studies classified osteoporotic versus normal patients, ( 29,36,39,40,43,49,50,52,54–57,62 ) osteoporotic versus non‐osteoporotic patients (based on a BMD T ‐score threshold of –2.5 SD), ( 34,38,44,64 ) normal versus abnormal subjects (based on the BMD T ‐score threshold of −1 SD), ( 33,41,42,45,47,48,58–60,65 ) experimented multiple classifications, ( 46,63 ) or assigned to three classes: osteoporosis (BMD T ‐score ≤ −2.5 SD), osteopenia (−2.5 < BMD T ‐score ≤ −1), and normal (BMD T ‐score > −1 SD).…”

“…( 44 ) An image enhancement and standardization step, and combining multiple features, was able to considerably improve results in two studies, respectively. ( 43,61 ) Overfitting was addressed by cropping images into regions, ( 32,34,37,38,44–46,53,57,59 ) dimensionality reduction to select best features, ( 39,40,43,56,64 ) data augmentation, ( 40,54 ) or by using a previously trained model. ( 38,49,56 ) Curiously, a simple base model using age and body mass index (BMI) did not perform better than random guess (AUC ≈ 0.5), suggesting that the population had undergone a selection process resulting in an imbalance for these variables.…”

“…Some studies did not even clearly state their definition of ground truth, a likely cause for some extremely optimistic and unrealistic results. ( 36,45,52,57,61–64 ) Lack of a reference standard makes it difficult to accurately evaluate the clinical implications of the proposed ML solution.…”

Evaluation of low bone mass and prediction of fracture risk using metacarpal radiogrammetry method: a comparative study withDXAand X‐ray phantom

“…Areeckal et al . performed automated segmentation in the third metacarpal bone shaft and extracted the radiogrammetric features and classified normal and low bone mass using artificial neural network classifiers.…”