Table 7 Main studies in the use of artificial intelligence as an aid to the diagnosis of osteoporosis
From: Artificial intelligence on the identification of risk groups for osteoporosis, a general review
Author/article | Conventional method | ||||||
|---|---|---|---|---|---|---|---|
Y | AI | % | VAR | PAC | Country | Gender | |
Kung et al. [50] | 2002 | OSTA | 91.0 | 22 | 722 | China | F |
Rizzi et al. [15] | 2004 | MoG | N/A | 3 | 845 | Italy | F |
Wenjia et al. [16] | 2005 | Hybrid | 85.7 | 5 | 2.158 | Iran | F |
Chiu et al. [18] | 2006 | ANN | 79.2 | 7 | 1.403 | Taiwan | M/F |
Leslie et al. [23] | 2009 | Algorithm | 93.3 | 5 | 4.015 | Canada | F |
Mantzaris et al. [24] | 2010 | LVQ | 96.6 | 4 | 3.426 | Greece | M/F |
Cos Juez et al. [25] | 2010 | MLP | 97.9 | 10 | 200 | Spain | F |
Jennane et al. [44] | 2012 | SVM | 87.0 | 20 | 69 | Argentina | F |
Harrar et al. [26] | 2012 | MLP | 97.0 | 5 | 120 | France | F |
Yoo et al. [27] | 2013 | SVM | 76.7 | 11 | 1674 | South Korea | F |
Anburajan et al. [28] | 2013 | SVM | 90.0 | 5 | 50 | India | F |
Kavitha et al. [29] | 2013 | SVM | 91.8 | 3 | 100 | Japan | F |
Tafraouti et al. [30] | 2014 | SVM | 93.0 | 16 | 77 | France | M/F |
Iliou et al. [31] | 2015 | MLP | 83.0 | 35 | 589 | Greece | M/F |
Liu et al. [32] | 2015 | MLP | 93.0 | 10 | 725 | Taiwan | M/F |
Xinghu et al. [33] | 2016 | ANN | 95.0 | 17 | 119 | China | M/F |