Current AI applications in the diagnosis of childhood non-solid tumor
| AI method | Medical field | Task | Tumor | Result | |
|---|---|---|---|---|---|
| CNN/GAN | Pathology | Detecting ALL and AML using a deep learner classifier using microscopic blood images | ALL and AML | ACC: 98%–98.67% | [20, 21] |
| CNN and GAN | Pathology/Genomics | Constructing a hybrid model using a genetic algorithm and a residual CNN to predict ALL using microscopy images | ALL | ACC: 98.46% | [26] |
| SVM | Pathology | Building a model to classify acute leukemias using flow cytometry | Acute promyelocytic leukemia | ACC: 94.2%; AUC: 99.5 | [22] |
| ANN/FFNN/SVM | Pathology | Proposing a ML-based model for ALL categorization using microscopic blood images | ALL | ACC: 98.1–100% | [23, 24] |
| CNN | Pathology | Building an aggregated DL model for leukemic B-lymphoblast classification | Leukemic B-lymphoblast | ACC: 96.58% | [25] |
| CNN | Pathology | Using bone marrow cell microscopy images for the classification of AML, ALL, and CML | AML, ALL, and CML | ACC: 90–99% | [27] |
| RF | Others-mRNA sequencing | Developing transcriptome-wide biomarkers for ALL subtyping | ALL | ACC: 97–100% | [28] |
| ANN | Others-DNA methylation | Identifying reliable cancer-associated methylation signals in gene regions from leukemia patients | Leukemia | ACC: 93.8% | [29] |
| Nearest shrunken centroids | Others-DNA methylation | Investigating the utility of CpG methylation status to differentiate blood from patients with ALL and AML from normal blood | ALL and AML | AUC: 99.98 | [30] |
GAN: generative adversarial network; SVM: support vector machine; ANN: artificial neural network; FFNN: feed forward neural network; ACC: accuracy; AUC: area under the curve; RF: random forest; CpG: cytosine-guanine
