. 2022 Jul 14;4(3):136–142. doi: 10.1097/BS9.0000000000000130

Table 2.

AI application in hematologic lymphoid malignancies.

Study	Publication year	Disease	Digital image type	Model	Training set size	Valuation set size	Performance evaluation
Putzu et al²⁴	2014	ALL	Microscopic images	SVM, K-NN, NB, DT	267	NA	93.63% accuracy
Neoh et al²⁵	2015	ALL	Microscopic images	Multi-layer perceptron, SVM	180	18	96.72% accuracy
Alferez et al²⁶	2015	FL, HCL, CLL, MCL	Microscopic images	SVM	4389	439	97.67% accuracy
MoradiAmin et al²⁷	2016	ALL	Microscopic images	SVM	5625	625	96.76% accuracy, 94.23% precision
Shafique et al²⁸	2018	ALL	Microscopic images	AlexNet	221	147	96.06% accuracy
Rehman et al²⁹	2018	ALL	Microscopic images	NB, SVM, CNN	264	66	97.78% accuracy
El Achi et al³⁰	2019	Lymphoma	Microscopic images	CNN	1856	464	95% accuracy
Sahlol et al³¹	2020	ALL	Microscopic image	VGGNet	8737	2184	97.1% accuracy
Mohlman et al³²	2020	Diffuse large B-cell lymphoma	H&E staining images	CNN	8796	2022	0.92 AUC
Syrykh et al³³	2020	FL	Microscopic images	Bayesian neural network	160,000	80,000	0.99 AUC

AI = artificial intelligence, ALL = acute lymphoblastic leukemia, CLL = chronic lymphocytic leukemia, CNN = convolutional neural network, DT = Decision Trees, FL = follicular lymphoma, HCL = hairy cell leukemia, K-NN = K-Nearest Neighbor, MCL= mantle cell leukemia, NB = Naïve Bayesian, RF = Random Forest, SVM = Support Vector Machines.