or253f-T1

Table 1: Summary of deep learning techniques applied to orthopedic literature.

Study

Aims

Deep learning method

Application

Staartjes,

et al.5

Evaluate the feasibility of deriving robust deep learning-based predictive analytics from single-center, single-surgeon data

Deep neural network-based and logistic regression-based prediction models

Basic AI education Outcomes assessment

Kang,

et al.7

Develop a machine learning–based implant recognition program and to verify its accuracy

Object detection and clustering. Model training with Keras deep learning platform

Basic AI education

Moon,

et al.14

Automatically, rapidly, and accurately detect lower limb alignment by using anteroposterior standing X-Ray

YOLOv5 (Vision AI)

HarDNet-MSEG image segmentation model

Radiographic assessment

Tan,

et al.23

Identify biomarkers and develop an integrated diagnostic model for predicting the onset of early intervertebral disc degeneration

LASSO, random forest, and support vector machine recursive feature elimination

Basic science

Anastasio,

et al.24

Identify combinations of orthobiologic factors applied to bone healing/fusion

Artificial neural networks

Basic science

Basic AI education

Yan,

et al.25

Segment chondrocytes from histological images of cartilage

U-Net (convolutional neural network)

Basic science

Melgoza,

et al.26

Report a new robust quantitative mouse intervertebral disc degeneration histopathological scoring system

Artificial neural networks and multilayer perceptron

Basic science

Zhu,

et al.28

Develop a predictive model for postoperative osteonecrosis of the femoral head

MATLAB convolutional neural network

Basic AI education outcomes assessment

Maki,

et al.35

Develop a predictive model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament

Machine learning, LightGBM, deep learning, RadImagenet

Spine-oriented surgery

Radiographic assessment

Patel,

et al.36

Develop and evaluate a model for identifying orthopedic implants using radiographs

Seven convolutional neural networks

U-Net segmentation network

Basic AI education Radiographic assessment

Shen,

et al.37

Developed an MRI-based system to detect early osteonecrosis of the femoral head

Convolutional neural network

Radiographic assessment

Guy,

et al.38

Make the diagnosis of proximal femur fracture on radiographs

Deep learning algorithm (TensorFlow)

Radiographic assessment

Guo,

et al.39

Developed a model for diagnosing supraspinatus tears shoulder MRI

Convolutional neural networks (Xception)

Radiographic assessment

Yi,

et al.40

Identification, classification and differentiation for knee arthroplasty implants

Deep convolutional neural network

Radiographic assessment

Klempt,

et al.41

Develop and validate a model for identification of hip and knee joint arthroplasty designs from plain radiographs

Convolutional neural network

Radiographic assessment

Merkely,

et al.42

Identify cartilage defects when applied to the interpretation of knee MRI

Three convolutional neural networks

Radiographic assessment

Yamamoto,

et al.43

Statistically evaluate the osteoporosis identification ability

ResNet convolutional neural network

Radiographic assessment

Tiwari,

et al.44

Identify the most appropriate -based model for the detecting grade of knee osteoarthritis

Eight machine learning models (DenseNet)

Radiographic assessment

Basic AI education

Leung,

et al.45

Develop a prediction model for risk of knee osteoarthritis progression

Transfer learning approach (ResNet34)

Radiographic assessment

Borjali,

et al.46

Develop a model for efficient and accurate hip dislocation detection following primary total hip replacement from medical narratives

Convolutional neural network natural language processing models

Basic AI education

Eweje,

et al.47

Develop an algorithm that can differentiate benign and malignant bone lesions using routine MRI

EfficientNet-B0 architecture and a logistic regression model

Radiographic assessment

Ashkani,

et al.48

Assess the performance of two different networks in detecting ankle fractures using radiographs

Deep convolutional neural networks

Inception V3 and ResNet50

Radiographic assessment

Li,

et al.49

Improve the diagnostic accuracy and efficiency for diagnosing meniscal tear using MRI

Mask regional convolutional neural network. ResNet50

Radiographic assessment

Magneli,

et al.50

Train and evaluate a model for AO/OTA classification of shoulder fractures

A modified CNN of the ResNet architecture

Radiographic assessment

Shen,

et al.51

Exploratory investigation for the classification and prediction of mechanical states of cortical and trabecular bone tissue

Convolutional neural networks. ResNet with transfer learning

Basic science

Basic AI education

Lau,

et al.52

Build an image-based machine-learning model for detecting TKA loosening

Random forest classifier

Xception Model, ImageNet and TensorFlow

Radiographic assessment

Basic AI education

Kim,

et al.53

To automatically select and position THA components that are most suitable for the patient’s bone anatomy

Convolutional neural network

Basic AI education

Recht,

et al.54

Accelerate MRI to allow a 5-minute comprehensive examination of the knee

Variational network

U-Net

Basic science

Basic AI education

Borjali,

et al.55

Increase accuracy, accelerate analysis time, and reduce interobserver bias by automating 3D volume assessment of syndesmosis anatomy

Three deep learning models

Radiographic assessment

Basic AI education

Yang,

et al.56

To assess the severity of knee osteoarthritis in portable devices

RefineDet

Deep learning-based diagnostic model

Basic AI education

Radiographic assessment

Hernigou,

et al.57

Provide an overview of the possibility to predict dislocation with a calculator according to the type of implant for THA

Supervised learning model Artificial neural network

Basic AI education

Outcomes assessment

Rahman,

et al.58

To detect loosening of the hip implant using X-Ray images

Deep Convolutional Neural Networks based novel stacking approach (HipXNet)

Basic AI education

Radiographic assessment

Wang,

et al.59

Develop a recovery and nursing system after total hip arthroplasty and to conduct clinical trials

Deep neural network based on Mask R-CNN

Basic AI education

Outcomes assessment

Kinugasa,

et al.60

Evaluate the accuracy of diagnoses made by AI on ultrasound images of developmental dysplasia of the hip

MATLAB deep learning toolbox

Basic AI education

Radiographic assessment

Sharifi,

et al.61

Identify spatiotemporal gait parameters, gait patterns, activity types, and changes in mobility after total knee arthroplasty

Six contemporary multivariate time series neural network architectures

Basic AI education

Outcomes assessment

Li,

et al.62

Evaluate the performance of DL in differentiation of benign and malignant vertebral fracture on CT

ResNet50 network

Radiographic assessment

Rouzrokh,

et al.63

Identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants

Two deep-learning algorithms

EfficientNetB3 classifier YOLOv5 object detector

Basic AI education

Radiographic assessment

Huang,

et al.64

Automated segmentation and quantification of the vertebrae and intervertebral discs on lumbar spine MRIs

Deep learning-based program (Spine Explorer)

Radiographic assessment

Spine-oriented surgery

Kong,

et al.65

Develop an X-Ray-based fracture prediction model using deep learning with longitudinal data

Convolutional neural network. DeepSurv

Radiographic assessment

Spine-oriented surgery

Zhao,

et al.66

Create a reliable learning-based approach that provides consistent and highly accurate measurements of the Cobb angle

Convolutional neural network

Deep learning SpineHRformer

Radiographic assessment

Wang,

et al.67

Analytic function for the correlation between lumbar disc herniation and angle and irregular variation of joint of lumbar facet-joint

Convolutional neural network-Based MRI image recognition algorithm

Radiographic assessment

Spine-oriented surgery

Broida,

et al.68

To more accurately screen surgical candidates seen in a spine clinic.

Transformer-based machine learning architecture

Spine-oriented surgery

Basic AI education

Ito,

et al.69

Predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament

Deep learning was used to create two predictive models

Spine-oriented surgery

Outcomes assessment

Etzel,

et al.70

Predict and classify whether a patient will experience a short or long hospital LOS after lumbar fusion surgery with a high degree of accuracy

Six machine learning algorithmic analyses

Spine-oriented surgery

Outcomes assessment

Maras,

et al.71

Differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain

Convolutional neural networks VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks

Radiographic assessment

Campagner,

et al.72

Define an invasiveness score for LBP procedures based on biological markers and inflammatory profiles

Supervised machine learning

Spine-oriented surgery

Outcomes assessment

Liu,

et al.73

Diagnosis of benign and malignant spinal tumors based on magnetic resonance imaging

Multimodel weighted fusion framework

Radiographic assessment

Müller,

et al.74

Predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine

Convolutional neural network

Spine-oriented surgery

Outcomes assessment

Mandel,

et al.75

Forecasting the outcome of vertebral body growth modulation from skeletally immature patients

Spatial-temporal corrective networks

Spine-oriented surgery

Outcomes assessment

Fan,

et al.76

Simulated foraminoplasty of percutaneous endoscopic transforaminal discectomy

Deep learning-derived 3D (DL-3D) models

Spine-oriented surgery

Chen,

et al.77

Analyze perioperative factors and predict the occurrence of surgical site infection following posterior lumbar spinal surgery

LASSO regression analysis, support vector machine, and random forest

Spine-oriented surgery

Outcomes assessment

Mu,

et al.78

Application value of magnetic resonance spectroscopy and computed tomography in the treatment of lumbar degenerative disease and osteoporosis

Deep convolutional neural network image segmentation processing technology

Spine-oriented surgery

Basic AI education

Cho,

et al.79

Automatically detect the tip of the instrument, localize a point, and evaluate the detection accuracy in biportal endoscopic spine surgery

RetinaNet and YOLOv2

Spine-oriented surgery

Basic AI education

Silva,

et al.80

Predict spine surgery outcome

Boosted decision tree classifier (SpineCloud)

Spine-oriented surgery

Kuris,

et al.81

Determine whether it could predict readmission after 3 lumbar fusion procedures

Neural network, a supervised machine learning technique

Spine-oriented surgery

Outcomes assessment

von Atzingen,

et al.82

Marker-less surgical navigation proof-of-concept to bending rod implants

Augmented reality with on-device machine learning

Spine-oriented surgery

Basic AI education

Tran,

et al.83

Semantic segmentation on X-Ray images

Multipath convolutional neural network, BiLuNet

Basic AI education

Radiographic assessment

Chen,

et al.84

Identify the possibility of THR in three months of hip joints by plain pelvic radiography

Sequential two-stage deep learning algorithm HipRD and SurgHipNet

Basic AI education

Radiographic assessment

Niculescu,

et al.85

Comparative study of the biomechanical behavior of commonly used orthopedic implants for tibial plateau fractures

Artificial Neural Network model

Basic science

Basic AI education

Bonnheim,

et al.86

Calculating biomarkers of cartilage endplate health using MRI images

Four independent convolutional neural networks

Basic science

Radiographic assessment

Kasa,

et al.87

Assess surgical performance with comparable performance to the expert human raters

Multimodal deep learning model

Basic AI education

Outcomes assessment

Loftus,

et al.88

Reproducibility of an automated postoperative triage classification system

Deep convolutional neural network

Basic AI education