Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Table 1 Terms commonly used to describe artificial neural network structures

Term of ANN	Specific meaning
Size	Number of neurons in the whole model
Width	Number of neurons in the one layer
Depth	Number of layers
Framework	Arrangement methods of layers and neurons
Capability	The reflection contents of reality by the specific model

ANN: Artificial neural networks.

Table 2 Comparisons between feedforward and feedback neural network

Category	Feedforward neural network	Feedback neural network
Signal direction	Unidirectional	Unidirectional/bidirectional
Operation time	Short	Long
Feedback by output signal	No	Yes
Structural complexity	Simple	Complicated
Memory time	Short-term or none	Long-term
Applied ranges in medicine	Wide	Limited
Application	Perceptron network, back propagation network, radial basis function network	Recurrent neural network, Hopfieid network, Boltzmann machine

Table 3 Summary of studies concerning artificial neural network translation of basic achievements

Ref.	Disease	Type of data	ANN technique	Application direction	Outcome
Bao et al[167], 2020	CRC	Microsatellite instability from TCGA database	Multi-layer perceptron network	Prognostic prediction	100% accuracy
Coppedè et al[189], 2015	CRC	DNA methylation	AutoCM	Identification of connections between DNA methylation and CRC	A strong connection between the low methylation levels ofthe five CRC genes
Liu et al[190], 2004	CRC	Gene signature from GEDatasets	Multi-layer network	Identification of latent marker genes of CRC	91.94% accuracy
Berishvili et al[164], 2019	CRC	Approximately 4000 complexes for which the data on the target binding constants	CNN	Screening filter for compoundprioritization	73% Spearman rank correlation coefficient
Bloom et al[159], 2007	CRC and GC	MS	Multi-layer network	Differentiation between 6 common tumor types	87% accuracy
Dadkhah et al[120], 2019	colorectal polyp	Gut microbiome	ANN developed by Orange data mining tool	Early screening using collected stool	> 75% accuracy
Chang et al[166], 2011	CRC	miRNA profile	Not mentioned	Exploration of association between specific miRNAs and clinicopathological features	100% accuracy of miRNA panel
Chen et al[191], 2004	CRC	MS of serum protein pattern	Multi-layer perceptron network	Differentiation between CRC and healthy control	91% sensitivity; 93% specificity; 0.967 AUC
He et al[121], 2020	CRC and gastroesophageal cancer	Gene signature from TCGA database	Multi-layer network	Differentiation between types of cancer	CRC: 98.06% sensitivity; 96.88% precision. Gastroesophageal cancer: 94.89% sensitivity; 96.33% precision
Hu et al[192], 2015	CRC	Gene signature from database of NCBI NLM NIH	S-Kohonen neural network	Prediction of recurrence using gene expressions	91% accuracy
Kurokawa et al[128], 2005	CRC	Gene signature of nodal metastasis	BNN	Prediction of metastatic potential of CRC at stage I	88.0% sensitivity; 86.6% specificity; 0.904 AUC
Liu et al[160], 2019	Cancer cell	Synthetic microscopic images from two publicly datasets	CNN	Automated counting of cancer cells	-
Ronen et al[193], 2019	CRC	Gene signature from TCGA database	BNN	Stratification of CRC subtypes	-
Bilsland et al[194], 2015	CRC	A virtual library of compounds	Perceptron network	Screen of Benzimidazolone inhibitors for CRC treatment	CB-20903630 was selected out for further validation of CRC treatment
Maniruzzaman et al[195], 2019	CRC	Gene signature from patients	Fuzzy neural network	CRC classification	99.84% sensitivity; 99.75% specific; 99.81% accuracy; 0.9995 AUC
Inglese et al[196], 2017	CRC	3D MS	Deep neural network (unsupervised)	Identification of metabolic heterogeneity	Up to 0.6991 Pearson's correlation
Shi et al[197], 2020	CRC with liver metastasis	CT	ANN	Prediction of KRAS, NRAS and BRAF status	0.95 AUC
Jiang et al[198], 2020	GC	Two drug datasets	deep neural network	Prediction of drug-disease associations	17 kinds of drugs that were screened out by ANN had been confirmed as anti-tumor drugs
Bidaut et al[158], 2009	Stomach stem cell	Stemness signature	Perceptron network	Characterization of stem cells	-
Jing et al[168], 2019	Calibration of laboratory markers	CA-724	Radial basis function neural network	The effects of geographic factors on CA-724	CA724 reference values show spatial autocorrelation and regional variation
Xiao et al[122], 2018	GC	RNA-seq	Probabilistic neural networks (semi- supervised)	Diagnosis of cancer	96.23% accuracy; 99.08% precision
Hang et al[144], 2018	GC	MSI	Multi-layer perceptron network	Prognostic prediction	0.81 AUC
Xuan et al[161], 2019	GC	LncRNA profile	CNN	Prediction of GC	0.977 AUC
Joo et al[163], 2019	GC	Potential drugs from databases	CNN	Exploration of new drugs targeting	ANN-based model accurately predicts drug responsiveness as models previously reported
Liu et al[165], 2010	GC	MS from GC patients	Supervised neural network	Early screening	100% sensitivity; 75% specificity
Que et al[199], 2019	GC	MS from GC patients and clinicopathological parameters	Single-layer neural network	Prediction of long-term survival	0.82 AUC
Li et al[200], 2021	GC	Gene Expression Omnibus database	ANN	Differentiation between GC and healthy tissues	0.946 AUC

TCGA: The Cancer Genome Atlas; CRC: Colorectal cancer; GC: Gastric cancer; CNN: Convolutional neural network; BNN: Bayesian neural network; AUC: Area under the curve; MS: Mass spectroscopy; MSI: Microsatellite instability; ANN: Artificial neural networks; CT: Computed tomography; IHC: Immunohistochemistry; NCBI: National Center for Biotechnology Information; BRAF: V-raf murine sarcoma viral oncogene homolog B; NLM: Nonlinear-mirror.

Table 4 Summary of existing studies of artificial neural networks applied in inflammatory bowel disease

Ref.	Disease	Aim	Number of samples	ANN technique	Included variables	Outcome
Ahmed et al[169], 2017	CD	Diagnosis	144 CD patients; 243 HC individuals	BPNN	103 variables	Accuracy 97.67%; sensitivity 96.07%; specificity 100%
Ananthakrishnan et al[154], 2017	UC and CD	Predicting treatment response to vedolizumab	43 UC patients; 42 CD patients	vedoNet	Gut microbiome	AUC of CD 88.1%; AUC of UC 85.3%
Anekboon et al[201], 2014	CD	Predicting single nucleotide polymorphisms	144 CD patients; 243 HC individuals	Multi-layer perceptron network	103 SNPs	Accuracy 90.4%; sensitivity 87.5%; specificity 92.2%
Dong et al[173], 2019	CD	Predicting the risk of surgical intervention in Chinese patients	83 patients with surgery; 83 patients without surgery	ANN	131 variables	Accuracy 90.89%; precision 46.83%; F1 score 0.5757
Fioravanti et al[202], 2018	IBD	Classification of metagenomics data	222 IBD patients; 38 HC individuals	CNN	Gut microbiota	-
Hardalaç et al[203], 2015	IBD	Predicting the effect of azathioprine on mucosal healing	129 IBD patients	BPNN	Age, age at diagnosis, usage of other medications prior to azathioprine use, smoking, sex, UC-CD	Accuracy 79.1%
Kirchberger-Tolstik et al[170], 2020	UC	Diagnosis	227 Raman maps with 567500 spectra	CNN	Images of Raman spectroscopy	sensitivity of 78%; specificity 93%
Klein et al[204], 2017	CD	Predicting the clinical phenotype	47 B1 patients; 19 B2 patients; 39 B3 patients	Two-layer FNN	H&E	B1 vs B2 phenotype: sensitivity 81%, specificity 74%, accuracy 75%, AUC 0.74; B1 vs B3 phenotype: sensitivity 69%, specificity 76%, accuracy 70.5%, AUC 0.78; B2 vs B3 phenotype: sensitivity 67%, specificity 72.5%, accuracy 69%, AUC 0.72
Lamash et al[71], 2019	CD	Visualization and quantitative estimation of CD	23 pediatric CD patients	CNN	MRI	DSCs of 75 ± 18%, 81 ± 8%, and 97 ± 2% for the lumen, wall, and background, respectively
Le et al[174], 2020	IBD	Predicting IBD and treatment status	68 CD patients; 53 UC patients; 34 HC individuals	Neural encoder-decoder (NED) network	Gut microbiota	CD vs HC: 95.2% AUC; UC vs HC: 92.5% AUC; CD vs UC: 81.8% AUC
Morilla et al[175], 2019	UC	Predicting treatment responses to infliximab for patients with acute severe UC	47 patients with acute severe ulcerative colitis	Deep neural network	MicroRNA profiles	84% accuracy; 0.82 AUC
Ozawa et al[112], 2019	UC	Identification of endoscopic inflammation severity	841 patients	CNN (GoogLeNet)	Colonoscopy images	0.86 AUC of Mayo 0; 0.98 AUC of Mayo 0-1
Peng et al[205], 2015	IBD	Predicting the frequency of relapse	569 UC patients; 332 CD patients	ANN	Meteorological data	High accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1 %)
Shepherd et al[171], 2014	IBD	Differential diagnosis between IBD and IBS	59 UC patients; 42 CD patients; 34 IBS patients; 46 HC individuals	Multi-layer perceptron neural network	Gas chromatograph coupled to a metal oxide sensor in stool samples	76% sensitivity, 88% specificity, 76% accuracy
Takayama et al[132], 2015	UC	Predicting treatment response to cytoapheresis	90 UC patients	Multi-layer perceptron neural network	13 clinical variables	96% sensitivity; 97% sensitivity
Tong et al[172], 2020	CD, UC and ITB	Differential diagnosis between CD, UC and ITB	5128 UC patients; 875 CD patients; ITB 396 patients	CNN	Differential features of endoscopic images between UC, CD and ITB	The precisions/recalls of UC-CD-ITB when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively

IBD: Inflammatory bowel disease; UC: Ulcerative colitis; CD: Crohn's disease; ITB: Intestinal tuberculosis; IBS: Irritable bowel syndrome; CNN: Convolutional neural network; BPNN: Back propagation neural network; FNN: Feedforward neural network; MSE: Mean square error; MAPE: Mean absolute percentage error; HC: Healthy control; AUC: Area under the curve; ANN: Artificial neural networks; SNPs: Single nucleotide polymorphisms; MRI: Magnetic resonance imaging; H&E: Haematoxylin-eosin.