Table 3 AI of included studies

Adams [11]

DCNN

AlexNet or GoogLeNet architectural model

X

Training-57%

Validation-29%

Test-14%

Urakawa [12]

CNN

VGG16 architectural model

X

Training-80%

Validation-10%

Test-10%

Cheng [13]

DCNN

DenseNet-121 architectural model

Image labeling and preprocessing = Each image was reviewed by a trauma surgeon for the preciseness of the label and quality of the images

Training-60%

Validation-20%

Krogue [14]

Deep learning model

DenseNet containing 169 layers architectural model

Object detection algorithm to place the bounding boxes automatically = Single-shot detector with the Resnet-50 feature pyramid network architecture

Training-60%

Validation-25%

Test-15%

Yu [15]

CNN

Inception-V3 architectural model

RoI identifying = Each ROI was either approved or revised by the local expert

Training-60%

Validation-20%

Test-20%

Mutasa [16]

CNN

Novel 2D neural

network utilizing a customized residual network based architecture

Highlight important regions = Gradient-weighted class activation mapping (Grad-CAM)

Training & validation-90%

Test-10%

Beyaz [17]

CNN

CNN containing GA blocks architectural model

Highlight important regions = Regions containing both fractured and non-fractured femoral necks were cropped from the X-ray images manually

X

Mawatari [18]

DCNN

GoogLeNet architectural model

RoI identifying = All radiographs were manually checked and annotated retrospectively by the three radiologists referring to CT and MRI for RoI selection

Training-85%

Test-15%

Yamada [19]

CNN

Xception architectural model

Highlight important regions = Orthopedic surgeon (3 years of experience) performed the image preprocessing using Paint 3D (Microsoft Corp, Redmond, WA, USA) by cropping the minimum region containing the femoral head and greater and lesser trochanters

Training-95%

Validation-5%

Cheng [20]

DCNN

DenseNet-121 architectural model

Highlight important regions = Grad-CAM

Training-60%

Validation-20%

Test-20%

Yoon [8]

Deep faster R-CNN

Math-Works (VGG-16 architecture) architectural model

Highlight important regions = Grad-CAM

Training-80%

Test-20%

Sato [1]

CNN

EfficientNet-B4 architectural model

Highlight important regions = Grad-CAM

Training-80%

Validation-10%

Test-10%

Bae [21]

CNN

Modified spatial attention module (CBAM + +) and ResNet18 architectural model

Highlight important regions = Grad-CAM

Training-80%

Validation-10%

Test-10%

Murphy [7]

CNN1 and CNN2

GoogLeNet architectural model

RoI identifying = MATLAB Training Image Labeller Application (tool)

Training-60%

Validation-20%

Test-20%