Table 1 Study aims, injury to be detected and patient inclusion/exclusion criteria, organised by publication date
From: Artificial intelligence for radiological paediatric fracture assessment: a systematic review
Author, year | Country | Body part | Type of injury | Patient inclusion criteria | Patient exclusion criteria | Study aim |
|---|---|---|---|---|---|---|
Zhou, [36] | USA | Forearm | Plastic bowing deformities | Forearm radiographs of children aged 1–18 years with history of trauma | None stated | Development of a computer-aided detection application for plastic bowing deformity fractures in paediatric forearms |
Malek [32] | Malaysia | Lower limb (femur, tibia, fibula) | Any fracture | Radiographs of fractured femur, tibia or fibula in children < 12 years of age | None stated | Development of an artificial neural network to analyse normal (< 12 weeks) versus delayed healing time for paediatric lower limb fractures |
England [31] | USA | Elbow | Traumatic elbow joint effusions | Elbow radiographs of children aged 1–19 years attending the emergency department with history of blunt trauma. Lateral view of radiograph technically adequate | Images with cast applied, elbow dislocation/displacement, comminuted fracture, metallic surgical hardware | Detection of traumatic paediatric elbow joint effusions using a deep convolutional neural network |
Rayan [33] | USA | Elbow | Any elbow fracture | Elbow radiographs in children | None stated | Binomial classification of elbow fractures using a deep learning approach |
Choi [17] | South Korea | Elbow | Supracondylar fractures | Elbow radiographs (two views) in children with suspected supracondylar fracture | Follow-up imaging (only initial radiographs included) Non-supracondylar fractures Elbow dislocation Underlying bone dysplasia | Development of a dual input convolutional neural network for detection of supracondylar fractures |
Starosolski [34] | USA | Distal tibia | Most fracture types | Radiographs of the foot, ankle, tibia or fibula in children | Plastic bowing fractures or any fracture without discrete fracture line. Images with surgical fixation, cast or other alternative pathology than fracture | Development of a convolutional neural network for detection of tibial fractures |
Dupuis [30] | France | Appendicular skeleton | Any appendicular fracture type | Radiographs of any body part from consecutive patients < 18 years old with suspected trauma attending emergency department | Radiographs of the axial skeleton (skull, spine, chest) | External validation of a commercially available deep learning algorithm for appendicular fracture detection in children |
Zhang [35] | Canada | Distal radius | Any fracture type | Children aged < 17 years with unilateral distal radial tenderness following trauma with asymptomatic contralateral wrist as normal comparator | Existing cast over forearm, laceration of the forearm, open fractures, inability to tolerate ultrasound study, lack of time for scanning | Diagnostic accuracy of 3D ultrasound and use of artificial intelligence for detection of paediatric wrist injuries |
Tsai [58] | USA | Distal tibia | Corner metaphyseal fractures | Children aged < 1 years referred for suspected abuse | None stated, AP projections for normal and abnormal distal tibial radiographs included only | Develop and evaluate a machine learning based binary classification algorithm to detect distal tibial corner metaphyseal fractures on radiographic skeletal surveys performed for suspected infant abuse |