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For: Turner OC, Aeffner F, Bangari DS, High W, Knight B, Forest T, Cossic B, Himmel LE, Rudmann DG, Bawa B, Muthuswamy A, Aina OH, Edmondson EF, Saravanan C, Brown DL, Sing T, Sebastian MM. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicol Pathol 2020;48:277-94. [DOI: 10.1177/0192623319881401] [Cited by in Crossref: 16] [Cited by in F6Publishing: 19] [Article Influence: 5.3] [Reference Citation Analysis]
Number Citing Articles
1 Pischon H, Mason D, Lawrenz B, Blanck O, Frisk AL, Schorsch F, Bertani V. Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats. Toxicol Pathol 2021;49:928-37. [PMID: 33397216 DOI: 10.1177/0192623320983244] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
2 Giovagnoli MR, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards. Healthcare (Basel) 2021;9:858. [PMID: 34356236 DOI: 10.3390/healthcare9070858] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021;12:42. [PMID: 34881097 DOI: 10.4103/jpi.jpi_36_21] [Reference Citation Analysis]
4 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol 2021;:3009858211040484. [PMID: 34521285 DOI: 10.1177/03009858211040484] [Reference Citation Analysis]
6 Bertani V, Blanck O, Guignard D, Schorsch F, Pischon H. Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models. Toxicol Pathol 2021;:1926233211052010. [PMID: 34670459 DOI: 10.1177/01926233211052010] [Reference Citation Analysis]
7 Tokarz DA, Steinbach TJ, Lokhande A, Srivastava G, Ugalmugle R, Co CA, Shockley KR, Singletary E, Cesta MF, Thomas HC, Chen VS, Hobbie K, Crabbs TA. Using Artificial Intelligence to Detect, Classify, and Objectively Score Severity of Rodent Cardiomyopathy. Toxicol Pathol 2021;49:888-96. [PMID: 33287662 DOI: 10.1177/0192623320972614] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]
8 Zingman I, Zippel N, Birk G, Eder S, Thomas L, Schönberger T, Stierstorfer B, Heinemann F. Deep Learning-Based Detection of Endothelial Tip Cells in the Oxygen-Induced Retinopathy Model. Toxicol Pathol 2021;49:862-71. [PMID: 33896293 DOI: 10.1177/0192623320972964] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
9 Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med 2020;49:849-56. [PMID: 32449232 DOI: 10.1111/jop.13042] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Turner OC, Knight B, Zuraw A, Litjens G, Rudmann DG. Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology. Toxicol Pathol 2021;49:714-9. [PMID: 33590805 DOI: 10.1177/0192623321990375] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Aeffner F, Sing T, Turner OC. Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology. Toxicol Pathol 2021;49:705-8. [PMID: 33840332 DOI: 10.1177/0192623321993756] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Giovagnoli MR, Ciucciarelli S, Castrichella L, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders. Healthcare (Basel) 2021;9:1347. [PMID: 34683027 DOI: 10.3390/healthcare9101347] [Reference Citation Analysis]
13 Ramot Y, Zandani G, Madar Z, Deshmukh S, Nyska A. Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice. Toxicol Pathol 2020;48:702-7. [PMID: 32508268 DOI: 10.1177/0192623320926478] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
14 Heinemann F, Lempp C, Colbatzky F, Deschl U, Nolte T. Quantification of Hepatocellular Mitoses in a Toxicological Study in Rats Using a Convolutional Neural Network. Toxicol Pathol 2022;:1926233221083500. [PMID: 35321595 DOI: 10.1177/01926233221083500] [Reference Citation Analysis]
15 Davis AS, Chang MY, Brune JE, Hallstrand TS, Johnson B, Lindhartsen S, Hewitt SM, Frevert CW. The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues. J Histochem Cytochem 2021;69:137-55. [PMID: 32936035 DOI: 10.1369/0022155420959146] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
16 Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O'Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021;49:815-42. [PMID: 33618634 DOI: 10.1177/0192623320986423] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Srivastava A, Hanig JP. Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach. J Appl Toxicol 2021;41:996-1006. [PMID: 33140470 DOI: 10.1002/jat.4098] [Reference Citation Analysis]
18 Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol 2021:S0828-282X(21)00867-9. [PMID: 34813876 DOI: 10.1016/j.cjca.2021.11.008] [Reference Citation Analysis]
19 Rudmann D, Albretsen J, Doolan C, Gregson M, Dray B, Sargeant A, O'Shea D D, Kuklyte J, Power A, Fitzgerald J. Using Deep Learning Artificial Intelligence Algorithms to Verify N-Nitroso-N-Methylurea and Urethane Positive Control Proliferative Changes in Tg-RasH2 Mouse Carcinogenicity Studies. Toxicol Pathol 2021;49:938-49. [PMID: 33287665 DOI: 10.1177/0192623320973986] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
20 Ramot Y, Deshpande A, Morello V, Michieli P, Shlomov T, Nyska A. Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm. Toxicol Pathol 2021;49:1126-33. [PMID: 33769147 DOI: 10.1177/01926233211003866] [Reference Citation Analysis]
21 De Vera Mudry MC, Martin J, Schumacher V, Venugopal R. Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy. Toxicol Pathol 2021;49:851-61. [PMID: 33371793 DOI: 10.1177/0192623320980674] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
22 Marble HD, Huang R, Dudgeon SN, Lowe A, Herrmann MD, Blakely S, Leavitt MO, Isaacs M, Hanna MG, Sharma A, Veetil J, Goldberg P, Schmid JH, Lasiter L, Gallas BD, Abels E, Lennerz JK. A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients. J Pathol Inform 2020;11:22. [PMID: 33042601 DOI: 10.4103/jpi.jpi_27_20] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]