Table 1.

Artificial intelligence (AI) applications in multiple myeloma diagnosis, and bone lesions identification.

Diagnosis
Parameters	AI Tools	Ref.	Key Findings
Blood and biochemical exams	Gradient boosting decisional tree	[24]	A ML approach on standard laboratory findings enhances the percentage of early detection
Differential cell counts of bone marrow aspirate	VGG16 convolutional network	[25]	Bone marrow aspirate differential counts employing ML techniques
Cytofluorimetric analysis of bone marrow aspirate	FlowCAP	[26]	Computerized methods for cytofluorimetric analysis
	Gradient boosting machine technique	[27]	Classification of plasma cell dyscrasias by combining AI and flow cytometry
Laser-induced breakdown spectroscopy analysis	Quadratic discriminant analysis, k-Nearest Neighbour	[28]	Diagnosis of malignancies using serum-based laser induced breakdown spectroscopy and chemometric methods
	K-Nearest Neighbour, Support Vector Machine, Artificial Neural Networks	[29]	Diagnosis of malignancies using serum-based laser induced breakdown spectroscopy in combination with ML methods can serve as fast technique for MM diagnosis and staging
Bone Lesions Identification
Techniques	AI tools	Ref.	Key findings
PET and CT	Convolutional neural network (v-Net, w-Net)	[30]	68Ga-Pentixaflor PET/CT and DL techniques to detect MM whole-body bone lesions
PET and CT	Random Forest	[31]	Radiomics analysis of 18-FDG PET/CT image with ML overcame the limitations of visual analysis
MRI	Naïve Bayes, Support Vector Machine, k-Nearest Neighbour, Random Forest, Artificial Neural Networks	[32]	ML radiomics is able to differentiate between MM and metastasis subtypes of lumbar vertebra lesions
SELDI-TOF-MS (mass peaks with mass-to-charge ratios)	Random Forest, Partial least squares discriminant analysis	[33]	SELDI-TOF-MS and ML tools discriminate MM patients with and without skeletal involvement

SELDI-TOF-MS, Surface enhanced laser desorption/ionization time-offlight mass spectrometry.