Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs

Table 1 Data splits in training, testing and validation subsets according to Kellgren-Lawrence grades

Osteoarthritis Kellgren-Lawrence grade	Training		Testing		Validation
	Samples, n	Proportion, %	Samples, n	Proportion, %	Samples, n	Proportion, %
0	255	17.6	37	17.6	73	17.7
1	213	14.7	31	14.7	61	14.8
2	164	11.4	24	11.4	47	11.4
3	237	16.4	35	16.7	68	16.4
4	576	39.9	83	39.5	164	39.7
Total	1445	100	210	100	413	100

Table 2 Evaluation of parameters for knee osteoarthritis detection

Parameter
Accuracy	Determines the accuracy of the standalone model inaccuracy to detect the presence of KOA and its classification in the input image
Precision	True positive/true positive + false positive
Recall	True positive/true positive + false negative
Loss	Determines the loss of the model

KOA: Knee osteoarthritis.

Table 3 Performance comparison of various transfer learning convolutional neural network models and eight expert human interpretations used for the development of deep learning algorithm for orthopedic radiographs

Model name	Accuracy	Precision	Recall	Loss	Outcome
ResNet50	54.29%	61.03%	39.52%	1.06	Average
VGG-16	56.68%	67.56%	35.02%	1.10	Average
InceptionV3	87.34%	89.19%	85.67%	0.35	Good
MobilnetV2	82.15%	84.66%	80.21%	0.46	Average
EfficientnetB7	56.61%	70.09%	38.27%	0.98	Average
DenseNet201	92.87%	93.69%	92.53%	0.20	Best
Xception	82.81%	85.03%	77.05%	0.50	Average
NasNetMobile	80.90%	83.98%	77.30%	0.50	Average
Surgeon	74.22%	79.50%	50.00%	0.25	Good