Automated patellar height assessment on high-resolution radiographs with a novel deep learning-based approach

Table 1 Demographic information among the training and testing groups

	Total (n = 184)	Training group (n = 82)	Validation group (n = 10)	Testing group (n = 92)
Males/Females	65/119	26/56	4/6	35/57
Left/Right knee	99/85	47/35	3/7	49/43
Age (yr)	61.9 ± 17.83	64.0 ± 17.62	53.7 ± 11.21	61.1 ± 18.37

Table 2 The results of patellar height indexes calculated by two orthopedic surgeons and those achieved by artificial intelligence (based on a deep learning algorithm and neural network)

	CD index	BP index
Orthopedic surgeon (R#1)	0.93 ± 0.19	0.80 ± 0.17
Orthopedic surgeon (R#2)	0.89 ± 0.19	0.78 ± 0.17
Artificial intelligence	0.92 ± 0.21	0.75 ± 0.19

R#1: Senior specialist; R#2: Younger specialist; CD: Caton-deschamps; BP: Blackburne-Peel. Results of indexes are presented as means ± SD.

Table 3 Reliability assessed with the use of interclass correlation coefficient (± SD) and single measurement among the interobserver measurements

	ICC	SEM
CD index
R#1 vs AI	0.86 ± 0.38	0.015
R#2 vs AI	0.88 ± 0.38	0.015
BP index
R#1 vs AI	0.80 ± 0.33	0.013
R#2 vs AI	0.79 ± 0.32	0.014

R#1: Senior specialist; R#2: Younger specialist; AI: Artificial intelligence; SEM: Single measurement; CD: Caton-deschamps.