Current AI applications in the diagnosis of childhood extracranial tumor
| AI method | Medical field | Task | Tumor | Result | References |
|---|---|---|---|---|---|
| CNN | Radiology | Developing an AI algorithm to distinguish osteomyelitis from Ewing sarcoma | Ewing sarcoma and osteomyelitis | ACC: 86.7–94.4% | [53] |
| CNN/SVM | Radiology | Constructing image-based models to identify well-differentiated liposarcoma and lipoma | Well-differentiated liposarcomas and lipomas | ACC: 86.84%; AUC: 0.942 | [54] |
| CNN/RF | Radiology | Developing a DL/ML model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists | Malignant and benign bone tumors | AUC: 0.79–0.97 | [49–51] |
| SVM/GLM/RF | Radiology | Constructing a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses | Malignant and benign soft-tissue masses | AUC: 0.88–0.96; ACC: 80.8–90.5% | [52] |
| CNN | Pathology | Building CNNs for rhabdomyosarcoma histology subtype classification | Rhabdomyosarcoma | AUC: 0.92–0.94 | [55] |
| CNN | Pathology | Developing a DL CNN-based differential diagnosis system on soft-tissue sarcoma subtypes based on whole histopathology tissue slides | Soft-tissue sarcoma | AUC: 0.889 | [56] |
| LDA | Pathology | Identifying proteomic differences, which would more reliably differentiate between benign and malignant melanocytic lesions | Benign nevi and melanomas | SEN: 98.76%; SPE: 99.65% | [59] |
| CNN | Others-dermatological photos | Establishing an AI algorithm to diagnose infantile hemangiomas based on clinical images | Infantile hemangiomas | ACC: 91.7% | [61] |
| LR | Others-umbilical cord blood sera | Exploring prediction biomarkers for infantile hemangiomas using noninvasive umbilical cord blood | Infantile hemangiomas | AUC: 0.756–0.943 | [62] |
| CNN | Others-dermoscopic examination | Developing a DCNN model to support dermatologists in the classification and management of atypical melanocytic skin lesions | Early melanomas and atypical nevi | AUC: 0.903 | [60] |
| SVM | Others-cell-free DNA | Providing a comprehensive analysis of circulating tumor DNA beyond recurrent genetic aberrations for early diagnosis | Ewing sarcoma and other pediatric sarcomas | SEN: 73%; SPE: 100% | [57] |
| SVM | Others-electronic colorimeters | Determining the diagnostic utility of widely available colorimetric technology when differentiating port-wine birthmarks from infantile hemangiomas in photographs of infants less than 3 months old | Port-wine birthmarks and infantile hemangiomas | ACC: 90% | [63] |
| DT & RF | Others-array-generated DNA methylation data | Classifying soft tissue and bone tumors using an ML classifier algorithm based on array-generated DNA methylation data | Soft tissue and bone tumors | AUC: 0.999 | [58] |
GLM: general linear model; LDA: linear discriminant analysis; DCNN: deep CNN