Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology
Open accessAbstract
BACKGROUND: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. AIMS: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. METHODS: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. RESULTS: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. CONCLUSIONS: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Olsen et al. found CNNs obtained 99.0% accuracy on carefully selected image patches but only 52.3% on non-selected ones; on whole-slide images, accuracy was 92%, sensitivity 85%, and specificity 99%.
“They discovered that CNNs obtained 99.0% accuracy on carefully selected image patches while this was only 52.3% on non-selected ones. On WSI the performance was better as CNNs had an accuracy of 92%, sensitivity of 85% and specificity of 99%.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Results (Sensitivity and specificity analysis; page unavailable) report whole-slide balanced accuracies of 99.45% for nodular BCC, 99.4% for dermal nevi, and 100% for seborrheic keratoses—not the claimed 92%. They also report 100% sensitivity and 98.9% specificity for nodular BCC. The Methods discuss image patches but do not report the claimed 99.0% versus 52.3% patch comparison. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe performance decline identified by Olsen et al. confirms the generalizability gap noted in Hart et al., suggesting this constraint is systemic among CNN-based models rather than an isolated occurrence.
“The performance decline identified by, Olsen et al. (28) confirms the generalizability gap noted in Hart et al. (22) suggesting that this constraint is systemic among CNN-based models rather than an isolated occurrence.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Results (Sensitivity and specificity analysis; page unavailable) report 99.45%, 99.4%, and 100% balanced accuracy on the three test sets, not a performance decline. The Discussion (page unavailable) describes the train–test split as showing generalizability and performance on out-of-sample observations. It does not establish a systemic constraint among CNN models. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe scoping review captures how model performance reduces when datasets used are non-curated or in real-world scenarios, as seen in Hart et al. and Olsen et al.
“The scoping review also captures how model performance reduces when the datasets used are non-curated or in real-world scenarios, as seen in the works of Hart et al. (22) and Olsen et al. (28).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Results (Sensitivity and specificity analysis; page unavailable) report 99.45% and 99.4% balanced accuracy for two algorithms and 100% correct classification for seborrheic keratosis. The Discussion (page unavailable) says the seborrheic keratosis training and test set was more rigorous, yet it had the highest yield—the opposite of reduced performance on a more challenging set. The paper does not report a separate test on non-curated datasets or in real-world deployment. (LLM, conf=0.82)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredOlsen et al. used and compared CNNs for identifying dermatopathological diseases and found 99% accuracy on curated image patches, decreasing to 52.3% on non-curated patches.
“Technically, Olsen et al. (28) used and compared CNNs for identifying dermatopathological diseases and found that it gives 99% accuracy on curated image patches, but decreases to 52.3% on non-curated patches.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Methods (Algorithm development and training) describe CNNs used to identify dermatopathological lesions, and the Results (Sensitivity and specificity analysis) report 99.45% balanced accuracy for nodular BCC. The paper does not report a comparison of curated with non-curated image patches or a decrease to 52.3%; its reported accuracy concerns a different testing setup. Page numbers are not available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThis was especially true when circumstances displayed similar visual traits, highlighting the necessity for improved and more diverse data sets.
“This was especially true when the circumstances displayed similar visual traits, highlighting the necessity for improved and more diverse data sets.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Discussion (page unavailable) identifies diagnoses with similar visual appearances and says future data collection should include more look-alike distractors to strengthen the algorithms. It does not show that performance problems were especially pronounced for visually similar diagnoses; the Results (page unavailable) report high accuracy, including perfect classification in the seborrheic keratosis test set. (LLM, conf=0.82)
Experimental — accuracy not yet measured
The field of inflammatory dermatopathology is largely unexplored by AI systems.
“On the other hand, the field of inflammatory dermatopathology is largely unexplored by AI systems [11,16,17].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [16]
The Introduction and Discussion describe AI testing for three diagnoses—nodular basal cell carcinoma, dermal nevus, and seborrheic keratosis—but do not assess how extensively AI has explored inflammatory dermatopathology. A search for inflammatory dermatopathology found an inflammatory condition used as a distractor in Methods, not a statement about the field’s research coverage. (LLM, conf=0.72)
Experimental — accuracy not yet measuredIn 94% of cases, Spitz lesions were misdiagnosed as conventional melanocytic lesions.
“The study showed that, in 94% of the cases, the Spitz lesions were misdiagnosed as conventional melanocytic lesions”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [28]
The Results section reports classification of nodular basal cell carcinoma, dermal nevi, and seborrheic keratoses, not Spitz lesions. The Discussion mentions Spitz nevi only as possible distractors for future training. Searches for the claimed 94% Spitz-lesion misdiagnosis found no such result in our copy; page numbers were not available. (LLM, conf=0.77)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fully convolutional networks for semantic segmentationPreprint
Long, J., Shelhamer, E., & Darrell, T. (2014) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1605.06211 · Cited by 43,601 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases
Janowczyk, A. R., & Madabhushi, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.186902 · Cited by 1,355 · see citing works
Image analysis and machine learning in digital pathology: challenges and opportunities
Madabhushi, A., & Lee, G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.06.037 · Cited by 1,093 · see citing works
Accelerating Very Deep Convolutional Networks for Classification and Detection
Zhang, X., Zou, J., He, K., & Sun, J. Z. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2502579 · Cited by 847 · see citing works
Artificial intelligence in medical practice: the question to the answer?
Miller, D. D., & Brown, E. W. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.10.035 · Cited by 774 · see citing works
Proceedings of the 22nd ACM international conference on Multimedia
(2014) · doi:10.1145/2647868 · Cited by 741 · see citing works
OpenSlide: A vendor-neutral software foundation for digital pathology
Goode, A., Gilbert, B., Harkes, J., Jukic, D., & Satyanarayanan, M. (2013) · Journal of Pathology Informatics · doi:10.4103/2153-3539.119005 · Cited by 546 · see citing works
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Veta, M., van Diest, P. J., Willems, S. M., Wang, H., Madabhushi, A., Cruz-Roa, Á., Gonzalez, F. A., Larsen, A., Vestergaard, J. S., Dahl, A. B., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.11.010 · Cited by 480 · see citing works
Rizzardi, A. E., Johnson, A. T., Vogel, R. I., Pambuccian, S. E., Henriksen, J. C., Skubitz, A. P. N., Metzger, G. J., & Schmechel, S. C. (2012) · Diagnostic Pathology · doi:10.1186/1746-1596-7-42 · Cited by 440 · see citing works
Review of the current state of whole slide imaging in pathology
Pantanowitz, L., Valenstein, P. N., Evans, A. J., Kaplan, K. J., Pfeifer, J. D., Wilbur, D. C., Collins, L. C., & Colgan, T. J. (2011) · Journal of Pathology Informatics · doi:10.4103/2153-3539.83746 · Cited by 435 · see citing works
Xu, Y. L., Jia, Z., Wang, L.-B., Ai, Y., Zhang, F., Lai, M., & Chang, E. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1685-x · Cited by 415 · see citing works
Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403 · see citing works
Digital pathology: current status and future perspectives
Al‐Janabi, S., Huisman, A., & van Diest, P. J. (2011) · Histopathology · doi:10.1111/j.1365-2559.2011.03814.x · Cited by 363 · see citing works
Deep learning for classification of colorectal polyps on whole-slide images
Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349 · see citing works
Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer
Golden, J. A. (2017) · JAMA · doi:10.1001/jama.2017.14580 · Cited by 297 · see citing works
The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics
Wei, Q., & Dunbrack, R. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067863 · Cited by 293 · see citing works
The Next Era: Deep Learning in Pharmaceutical Research
Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works
Thorstenson, S., Molin, J., & Lundström, C. F. (2014) · Journal of Pathology Informatics · doi:10.4103/2153-3539.129452 · Cited by 175 · see citing works
The Diagnostic Concordance of Whole Slide Imaging and Light Microscopy: A Systematic Review
Goacher, E., Randell, R., Williams, B. J., & Treanor, D. E. (2016) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0025-ra · Cited by 173 · see citing works
Validation of whole slide imaging for primary diagnosis in surgical pathology
Bauer, T. W., Schoenfield, L., Slaw, R. J., Yerian, L., Sun, Z., & Henricks, W. H. (2013) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2011-0678-oa · Cited by 155 · see citing works
Nielsen, P. S., Lindebjerg, J., Rasmussen, J., Starklint, H., Waldstrøm, M., & Nielsen, B. (2010) · Human Pathology · doi:10.1016/j.humpath.2010.05.015 · Cited by 91 · see citing works
Digital Pathology Consultations—a New Era in Digital Imaging, Challenges and Practical Applications
Lauro, G. R., Cable, W., Lesniak, A. J., et al. (2013) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-013-9572-0 · Cited by 88 · see citing works
Whole slide images for primary diagnostics in dermatopathology: A feasibility study
Al-Janabi, S., Huisman, A., Vink, A., Leguit, R. J., Offerhaus, G. J. A., ten Kate, F. J. W., van Dijk, M. R., & van Diest, P. J. (2012) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2011-200277 · Cited by 81 · see citing works
Diagnostic time in digital pathology: A comparative study on 400 cases
Vodovnik, A. (2016) · Journal of Pathology Informatics · doi:10.4103/2153-3539.175377 · Cited by 74 · see citing works
Introduction of the Thin Prep Imaging System™ (TIS): experience in a high volume academic practice
Chivukula, M., Saad, R. S., Elishaev, E., et al. (2007) · CytoJournal · doi:10.1186/1742-6413-4-6 · Cited by 70 · see citing works
Habeeb, A. A., Evans, A. J., & Ghazarian, D. (2012) · Journal of Pathology Informatics · doi:10.4103/2153-3539.93399 · Cited by 59 · see citing works
Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study
Kent, M. N., Olsen, T. G., Feeser, T. A., Tesno, K. C., Moad, J. C., Conroy, M. P., Kendrick, M. J. J., Stephenson, S. R., Murchland, M. R., Khan, A. U., et al. (2017) · JAMA Dermatology · doi:10.1001/jamadermatol.2017.3284 · Cited by 58 · see citing works
Self-citationAccelerating Innovation in Health IT
Rudin, R. S., Bates, D. W., & MacRae, C. A. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606884 · Cited by 51 · see citing works
Comparative diagnostic accuracy in virtual dermatopathology
Mooney, E., Hood, A. F., Lampros, J. N., Kempf, W., & Jemec, G. B. E. (2011) · Skin Research and Technology · doi:10.1111/j.1600-0846.2010.00493.x · Cited by 43 · see citing works
Randell, R., Ruddle, R. A., Mello-Thoms, C. R., et al. (2012) · Histopathology · doi:10.1111/j.1365-2559.2012.04323.x · Cited by 42 · see citing works
Histologic Diagnosis of Inflammatory Skin Diseases: An Algorithmic Method Based on Pattern Analysis,
Weedon, D. D. (1999) · American Journal of Dermatopathology · doi:10.1097/00000372-199912000-00017 · Cited by 40 · see citing works
Shah, K., Lehman, J. S., Gibson, L. E., Lohse, C. M., Comfere, N. I., & Wieland, C. N. (2016) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2016.08.024 · Cited by 38 · see citing works
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide ImagesPreprint
Korbar, B., Olofson, A. M., Miraflor, A. P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01550 · Cited by 37 · see citing works
Nielsen, P. S., Riber‐Hansen, R., Raundahl, J., et al. (2012) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2011-0360-oa · Cited by 34 · see citing works
Lee, J. J., Jedrych, J. J., Pantanowitz, L., & Ho, J. (2017) · American Journal of Dermatopathology · doi:10.1097/dad.0000000000000888 · Cited by 34 · see citing works
Wei, B., & Simpson, R. M. (2013) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2013.12.008 · Cited by 31 · see citing works
Accelerating Very Deep Convolutional Networks for Classification and DetectionPreprint
Zhang, X., Zou, J., He, K., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.06798 · Cited by 30 · see citing works
Automated Classification of Benign and Malignant Proliferative Breast Lesions
Radiya-Dixit, E., Zhu, D. C., & Beck, A. H. (2017) · Scientific Reports · doi:10.1038/s41598-017-10324-y · Cited by 27 · see citing works
A Comprehensive Analysis of a Web-Based Dermatopathology Second Opinion Consultation Practice
Zembowicz, A., Ahmad, A., & Lyle, S. R. (2011) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2010-0187-oa.1 · Cited by 24 · see citing works
Zhang, J., Song, Y., Xia, F., et al. (2017) · Medical Hypotheses · doi:10.1016/j.mehy.2017.08.021 · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [20]Kent MN Deploying a Digital Pathology Solution in a Dermatopathology Reference Lab. Abstract presented at: Association for Pathology Informatics Digital Pathology Workshop, Henry Ford Hospital 2017 12 8-9 Detroit, MI
- [36]Simonyan K Zisserman A Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv: 1409.1556 2014 · arXiv
- [37]Ashiquzzaman A Tushar AK Islam R Kim JM Kim K Kim H Baek N Reduction of overfitting in diabetes prediction using deep learning neural network IT Convergence and Security 2017. Lecture Notes in Electrical Engineering 2018 Singapore Springer
Cited by
Sorted by citations
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
Artificial intelligence applications in dermatology: Where do we stand?
Gomolin, A., Netchiporouk, E., Gniadecki, R., & Litvinov, I. V. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00100 · Cited by 142 · see citing works
Artificial intelligence in dermatopathology: diagnosis, education, and research
Wells, A., Patel, S., Lee, J. B., & Motaparthi, K. (2021) · Journal of Cutaneous Pathology · doi:10.1111/cup.13954 · Cited by 95 · see citing works
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64 · see citing works
Guo, L., Yang, Y., Ding, H., Zheng, H., Yang, H., Xie, J., Li, Y., Lin, T., & Ge, Y. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-1738 · Cited by 62 · see citing works
Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey
Polesie, S., McKee, P. H., Gardner, J. M., Gillstedt, M., Siarov, J., Neittaanmäki, N., & Paoli, J. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.591952 · Cited by 53 · see citing works
Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A. (2021) · Acta Dermato Venereologica · doi:10.2340/00015555-3893 · Cited by 51 · see citing works
Clinical protein science in translational medicine targeting malignant melanoma
Gil, J., Betancourt, L. H., Pla, I., Sanchez, A., Appelqvist, R., Miliotis, T., Kuras, M., Oskolas, H., Kim, Y., Horvath, Z., et al. (2019) · Cell Biology and Toxicology · doi:10.1007/s10565-019-09468-6 · Cited by 45 · see citing works
Artificial Intelligence in Dermatopathology: New Insights and Perspectives
Cazzato, G., Colagrande, A., Cimmino, A., Arezzo, F., Loizzi, V., Caporusso, C., Marangio, M., Foti, C., Romita, P., Lospalluti, L., et al. (2021) · Dermatopathology · doi:10.3390/dermatopathology8030044 · Cited by 37 · see citing works
Artificial Intelligence for Dermatopathology: Current Trends and the Road Ahead
Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.003 · Cited by 37 · see citing works
Doeleman, T., Hondelink, L. M., Vermeer, M. H., van Dijk, M. R., & Schrader, A. M. R. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.004 · Cited by 26 · see citing works
Artificial intelligence in dermatopathology: Updates, strengths, and challenges
Cazzato, G., & Rongioletti, F. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2024.06.010 · Cited by 24 · see citing works
Artificial intelligence in dermatopathology: A systematic review
Lalmalani, R. M., Lim, C. X. Y., & Oh, C. C. (2025) · Clinical and Experimental Dermatology · doi:10.1093/ced/llae361 · Cited by 19 · see citing works
Artificial intelligence: its role in dermatopathology
Jartarkar, S. R. (2022) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_725_2021 · Cited by 18 · see citing works
Artificial intelligence in Dermatopathology
Jartarkar, S. R., Cockerell, C. J., Patil, A., Kassir, M., Babaei, M., Weidenthaler‐Barth, B., Grabbe, S., & Goldust, M. (2023) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.15565 · Cited by 17 · see citing works
Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm
Sturm, B., Creytens, D., Smits, J., Ooms, A. H. A. G., Eijken, E., Kurpershoek, E., Küsters-Vandevelde, H. V. N., Wauters, C., Blokx, W. A. M., & van der Laak, J. A. W. M. (2022) · Diagnostics · doi:10.3390/diagnostics12020436 · Cited by 13 · see citing works
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“On the other hand, the field of inflammatory dermatopathology is largely unexplored by AI systems [11,16,17].”
Bao, Y., Zhang, J., Zhang, Q., Chang, J., Lu, D., & Fu, Y. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.696305 · Cited by 5 · see citing works
Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
Rammal, R., Mohy U Din, A., & Alam, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1614681 · Cited by 1 · see citing works
- Authors
- Olsen, T. G., Jackson, B. H., Feeser, T. A., Kent, M. N., Moad, J. C., Krishnamurthy, S., Lunsford, D. D., & Soans, R. E.
- Affiliations
- Wright State University (US)
- Corresponding author
- Kent, Michael N.
- ORCID
- Soans, Rajath E.
- Venue
- Journal of Pathology Informatics
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 9
- Issue
- 1
- Pages
- 32
- ISSN
- 2153-3539 (ISSN-L)
- PMC
- PMC6166480
- OpenAlex
- W2894398812
- Cited by
- 119
- References
- 44
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 14 · 2023: 13 · 2022: 21 · 2021: 14 · 2020: 17 · 2019: 12 · 2018: 1
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Pathology Informatics (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deep learning, dermatopathology, computer-aided diagnosis, nodular basal cell carcinoma, seborrheic keratosis, whole slide images, convolutional neural networks, diagnostic accuracy, skin lesions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
