Table 3:

Examples of image analysis studies primarily from the field of renal transplantation employing either hypothesis-driven/targeted or artificial intelligence (AI)/machine learning algorithms are shown.

Parameter(s) Assessed	Material/Stain(s) Assessed	Description	Ref.(s)
Hypothesis-Driven or Targeted Algorithms:
IF	TC (Masson), SR, and SMA IHC	IF IA on allograft biopsies correlated with GFR and urine total protein	43
IF	TC (Masson)	IF IA correlates with serum Cr in IgA nephropathy and membranoproliferative glomerulonephritis (MPGN)	44
IF	TC (Masson)	IF IA in renal allograft patients receiving cyclosporine correlated with worsened Cr	45
IF	TC (Light Green)	IF IA in renal allograft patients randomized to cyclosporine or conversion to sirolimus	46
IF	TC (Light green)	Quantitative IF in sequential renal allograft renal biopsies correlated with eGFR	47
IF	SR and collagen	Renal IF correlates with presence of TGF-β, decorin, SMA, and interstitial collagens	48–51
IF	SR	SR IA predicted long-term renal allograft function and time to graft failure	52
IF	SR	SR IA predicted long-term renal allograft function (decreased GFR)	53
IF	SR	IF was not significantly different between non-heart-beating and conventional heart-beating donor kidneys	54
IF	SR	IF scoring predicts survival and Cr in lupus nephritis	55
IF	SR	IA-based application (Fibrosis HR) for IF and glomerular morphometry	56
IF	SR	IF measurements using digital imaging coupled with point counting correlated with GFR	57
IF	SR	SR IF measurement combined with ultrasound measurements of renal artery resistance index helped predict “chronic allograft nephropathy” correlated with decreased GFR	58
IF	CIII IHC	IF by a semiautomatic system correlate with GFR in protocol renal transplant biopsies	59
IF	CIII IHC	IF measurements by a semiautomatic system correlate with GFR in protocol renal transplant biopsies	60
IF	TC (Masson)	IF IA and VA of cyclosporine (CsA) therapy effects	61, 62
IF	CIII IHC, TC, and SR	CIII IHC, TC, SR IA, and GFR correlated with each other and with VA	18
IF	TC, PAS, & IHC for CIII & CD34	Renal cortical and medullary IF, epithelial area, & microvessel density were correlated using IA and VA	19
Gloms	H&E, TC, PAS, Congo red, & Jones silver	Gabor filtering, Gaussian blurring, and statistical-based and other algorithmic steps were used to segment gloms in various stains	68
Artificial intelligence (AI)/machine learning algorithms
Gloms	H&E	Local binary pattern (LBP) support vector machine (SVM)-based glom detection	79
Gloms	Desmin IHC	Rectangular histogram of oriented gradients (Rectangular HOG) for glom detection	78
Gloms	Frozen H&E	Automated identification of sclerotic and nonsclerotic glomeruli using deep learning	83
Gloms	PAS	Diabetic glomerulosclerosis could be classified with CNNs	85
Gloms	PAS	CNN distinguished between Gloms & Non-Gloms	80
Gloms	TC	CNN segmentation of gloms	81
Gloms	TC	CNN localization of injured and noninjured gloms	82
Gloms, Tub, Int, Banff scoring	PAS	DL-based segmentation of Gloms, Tub, Int, other features, & Banff scores correlated with pathologist assessment	86
IF	TC	AI IF detection associates with renal survival using CNNs	88
Segmentation	H&E & PAS	“Human AI Loop (H-AI-L)” method decreased the annotation burden required of pathologists while still allowing for the AI-based segmentation of the kidney, prostate, & radiology data	84
Segmentation	H&E, PAS, TC, & Silver	DL segmentation of Gloms, Tub, arteries, arterioles, and peritubular capillaries	87

AI: artificial Intelligence, CIII: collagen III, CNN: convolutional neural networks, Cr: creatinine, DL: deep learning, eGFR: estimated GFR, GFR: glomerular filtration rate, Glom/Gloms: glomerulus (glomerular)/glomeruli, H&E: hematoxylin and eosin, IHC: immunohistochemistry, IF: interstitial fibrosis, IA: image analysis, Int: interstitium, MPGN: membranoproliferative glomerulonephritis, PAS: periodic acid–Schiff, Ref(s): references, SMA: smooth muscle actin, SR: Sirius red, TC: trichrome, TGF-β: transforming growth factor, Tub: tubules, VA: visual analysis.