Scientific publications

Publications
Posters

Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images

Siarov J, Siarov A, Kumar D, et al.

Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna

Siarov J, Kumar D, Paoli J, et al.

Deep learning assisted quantitative analysis of Aβ and microglia in patients with idiopathic normal pressure hydrocephalus in relation to cognitive outcome

Luikku AJ, Nerg O, Koivisto AM, et al.

CD163+ macrophages in mantle cell lymphoma induce activation of pro-survival pathways and immune suppression

Rodrigues JM, Lokhande L, Olsson LM, et al.

Limbic-predominant age-related TDP-43 encephalopathy in the oldest old: a population-based study

Mikhailenko E, Colangelo K, Tuimala J, et al.

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H)

Ercan C, Kordy K, Knuuttila A, et al.

Digital pathology structure and deployment in Veneto: a proof-of-concept study

Eccher A, Marletta S, Sbaraglia M, et al.

Democratizing Artificial Intelligence in Anatomic Pathology

Flotte TJ, Derauf SA, Byrd RK, et al.

Manually pressurized droplet digital PCR chip for rapid SARS-CoV-2 diagnostics

Elomaa P, Ojalehto T, Kumar D, et al.

Improved risk stratification scheme for mismatch repair proficient stage II colorectal cancers using the digital pathology biomarker QuantCRC

Wu C, Pai RK, Kosiorek H, et al.

Performance of an Artificial Intelligence Model for Recognition and Quantitation of Histologic Features of Eosinophilic Esophagitis on Biopsy Samples

Archila LR, Smith L, Sihvo HK, et al.

Deep learning quantification reveals a fundamental prognostic role for ductular reaction in biliary atresia

Nyholm I, Sjöblom N, Pihlajoki M, et al.

Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity

Westcott PMK, Muyas F, Hauck H, et al.

Ixodes scapularis nymph saliva protein blocks host inflammation and complement-mediated killing of Lyme disease agent, Borrelia burgdorferi

Bencosme-Cuevas E, Kim TK, Nguyen TT, et al.

MYC protein is a high-risk factor in mantle cell lymphoma and identifies cases beyond morphology, proliferation and TP53/p53 - a Nordic Lymphoma Group study

Rodrigues JM, Hollander P, Schmidt L, et al.

Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach

Köteles MM, Vigdorovits A, Kumar D, et al.

Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging

Maguluri G, Grimble J, Caron A, et al.

Visualizing Arc protein dynamics and localization in the mammalian brain using AAV-mediated in situ gene labeling

Avallone M, Pardo J, Mergiya TF, et al.

Deep learning based image analysis of liver steatosis in mouse models

Mairinoja L, Heikelä H, Blom S, et al.

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery

Boissin c, Laflamme L, Lundin M, et al.

Beneficial behavioral effects of chronic cerebral dopamine neurotrophic factor (CDNF) infusion in the N171-82Q transgenic model of Huntington’s disease

Stepanova P, Kumar D, Cavonius K, et al.

Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning

Vesterinen T, Säilä J, Blom S, et al.

High relative amount of nodular calcification in femoral plaques is associated with milder lower extremity arterial disease

Azeez M, Laivuori M, Tolva J et al.

A deep learning–based algorithm for tall cell detection in papillary thyroid carcinoma

Stenman S, Linder N, Lundin M et al.

Automated Image Analysis of Keratin 7 Staining Can Predict Disease Outcome in Primary Sclerosing Cholangitis

Sjöblom N, Boyd S, Manninen A, et al.

Changes in glial cell phenotypes precede overt neurofibrillary tangle formation, correlate with markers of cortical cell damage, and predict cognitive status of individuals at Braak III-IV stages

Taddei RN, Sanchez-Mico MV, Bonnar O, et al.

Development and Technical Validation of an Artificial Intelligence Model for Quantitative Analysis of Histopathologic Features of Eosinophilic Esophagitis

Archila LR, Smith L, Sihvo HK, et al.

Deep Learning-Based Segmentation of Morphologically Distinct Rat Hippocampal Reactive Astrocytes After Trimethyltin Exposure

Vuorimaa M, Kareinen I, Toivanen P, et al.

Quantitative pathologic analysis of digitized images of colorectal carcinoma improves prediction of recurrence free survival

Pai RK, Banerjee I, Shivji S, et al.

A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model

Stetzik L, Mercado G, Smith L, et al.

AI Model for Prostate Biopsies Predicts Cancer Survival

Sandeman K, Blom S, Koponen V, et al.

Aiforia Technologies Plc has received funding from the EU H2020 programme.

Evaluation of ABT-888 in the amelioration of α-synuclein fibril-induced neurodegeneration

Hastings L, Sokratian A, Apicco DJ, et al.

Distinct populations of highly potent TAU seed conformers in rapidly progressing Alzheimer’s disease

Kim C, Haldiman T, Kang SG, et al.

Microglia-like Cells Promote Neuronal Functions in Cerebral Organoids

Fagerlund I, Dougalis A, Shakirzyanova A, et al.

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer

Hondelink LM, Hüyük M, Postmus PE, et al.

Aiforia Technologies Plc has received funding from the EU H2020 programme.

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Zuraw A, Staup M, Klopfleisch R, et al.

MicroRNA-7 Protects Against Neurodegeneration Induced by α-Synuclein Preformed Fibrils in the Mouse Brain

Zhang J, Zhao M, Yan R, et al.

Differential impact of clinicopathological risk factors within the 2 largest ProMisE molecular subgroups of endometrial carcinoma

Pasanen A, Loukovaara M, Ahvenainen T, et al.

Functional diagnostics using fresh uncultured lung tumor cells to guide personalized treatments

Talwelkar SS, Mäyränpää MI, Søraas L, et al.

Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer

Westcott PMK, Sacks NJ, Schenkel JM, et al.

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone

Laury AR, Blom S, Ropponen T, et al.

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer’s disease and cerebral amyloid angiopathy

Perosa V, Scherlek AA, Kozberg MG, et al.

Monitoring of a progressive functional dopaminergic deficit in the A53T-AAV synuclein rats by combining 6-[18F]fluoro-L-m-tyrosine imaging and motor performances analysis

Becker G, Michel A, Bahri MA, et al.

Hantavirus infection-induced B cell activation elevates free light chains levels in circulation

Hepojoki J, Cabrera LE, Hepojoki S, et al.

Parvalbumin subtypes of cerebellar Purkinje cells contribute to differential intrinsic firing properties

Brandenburg C, Smith LA, Kilander MBC, et al.

Utilizing Deep Learning to Analyze Whole Slide Images of Colonic Biopsies for Associations Between Eosinophil Density and Clinicopathologic Features in Active Ulcerative Colitis

Vande Casteele N, Leighton JA, Pasha SF, et al.

Neuroprotective Potential of a Small Molecule RET Agonist in Cultured Dopamine Neurons and Hemiparkinsonian Rats

Renko JM, Mahato AK, Visnapuu T, et al.

Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models

Smith MA, Westerling-Bui T, Wilcox A, et al.

Deep neural network analysis - a paradigm shift for histological examination of health and welfare of farmed fish

Sveen L, Timmerhaus G, Johansen LH, Ytteborg E, et al.

Proof of Concept for a Deep Learning Algorithm for Identification and Quantification of Key Microscopic Features in the Murine Model of DSS-Induced Colitis

Bédard A, Westerling-Bui T, Zuraw A, et al.

Development and initial validation of a deep learning algorithm to quantify histologic features in colorectal carcinoma including tumour budding/poorly differentiated clusters

Pai RK, Hartman S, Schaeffer DF, et al.

Monocyte subset redistribution from blood to kidneys in patients with Puumala virus caused hemorrhagic fever with renal syndrome

Vangeti S, Strandin T, Liu S, et al.

Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens

Sjöblom N, Boyd S, Manninen A, et al.

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting

Holmström O, Linder N, Kaingu H, et al.

High tumor cell platelet-derived growth factor receptor beta expression is associated with shorter survival in malignant pleural epithelioid mesothelioma

Ollila H, Paajanen J, Wolff H, et al.

Glucocerebrosidase Gene Therapy Induces Alpha-Synuclein Clearance and Neuroprotection of Midbrain Dopaminergic Neurons in Mice and Macaques

Sucunza D, Rico AJ, Roda E, et al.

Neuroprotective Potential of a Small Molecule RET Agonist in Cultured Dopamine Neurons and Hemiparkinsonian Rats

Renko JM, Mahato AK, Visnapuu T, et al.

Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis

Mäkelä K, Mäyränpää M, Sihvo HK, et al.

Keap1 mutation renders lung adenocarcinomas dependent on Slc33a1

Romero R, Sánchez-Rivera FJ, Westcott PMK, et al.

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Srivastava A. & Hanig JP.

Fetal HLA-G mediated immune tolerance and interferon response in preeclampsia

Wedenoja S, Yoshihara M, Teder H, et al.

Neuropathological correlates of cortical superficial siderosis in cerebral amyloid angiopathy

Charidimou A, Perosa V, Frosch MP, et al.

Evaluating the optimum number of biopsies to assess histological inflammation in ulcerative colitis: a retrospective cohort study

Battat R, Casteele NV, Pai RK, et al.

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Holmström O, Stenman S, Suutala A, et al.

Osteoid metaplasia in femoral artery plaques is associated with the clinical severity of lower extremity artery disease in men

Laivuori M, Tolva J, Lokki AI, et al.

Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

Fortino V, Wisgrill L, Werner P, et al.

Using online game-based platforms to improve student performance and engagement in histology teaching

Felszeghy S, Pasonen-Seppänen S, Koskela A, et al.

Breast cancer outcome prediction with tumour tissue images and machine learning

Turkki R, Byckhov D, Lundin M, et al.

Detection of breast cancer lymph nodemetastases in frozen sections with a point-ofcare low-cost microscope scanner

Holmström O, Linder N, Moilanen H, et al.

Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours

Linder N, Taylor JC, Colling R, et al.

Downregulation of tyrosine hydroxylase phenotype after AAV injection above substantia nigra: Caution in experimental models of Parkinson’s disease

Albert K, Voutilainen MH, Domanskyi A, et al.

PTEN Loss but Not ERG Expression in Diagnostic Biopsies Is Associated with Increased Risk of Progression and Adverse Surgical Findings in Men with Prostate Cancer on Active Surveillance

Lokman U, Erickson AM, Vasarainen H, et al.

Synergistic neuroprotection by coffee components eicosanoyl-5-hydroxytryptamide and caffeine in models of Parkinson's disease and DLB

Yan R, Zhang J, Park H, et al.

Implementation of deep neural networks to count dopamine neurons in substantia nigra

Penttinen A, Parkkinen I, Blom S, et al.

The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study

Stenman S, Siironen P, Mustonen H, et al.

Reliability of histologic assessment in patients with eosinophilic oesophagitis

Warners MJ, Ambarus CA, Bredenoord AJ, et al.

Student-focused virtual histology education: Do new scenarios and digital technology matter?

Felszeghy S, Pasonen-Seppänen S, Koskela A, et al.

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Holmström O, Linder N, Ngasala B, et al.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

Turkki R, Linder N, Kovanen PE, et al.

Quantification of Estrogen Receptor-Alpha Expression in Human Breast Carcinomas With a Miniaturized, Low-Cost Digital Microscope: A Comparison with a High-End Whole Slide-Scanner

Holmström O, Linder N, Lundin M, et al.

Exploring viewing behavior data from whole slide images to predict correctness of students’ answers during practical exams in oral pathology

Walkowski S, Lundin M, Szymas J, et al.

Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis

Turkki R, Linder N, Holopainen T, et al.

Students' performance during practical examination on whole slide images using view path tracking

Walkowski S, Lundin M, Szymas J, et al.

A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears

Linder N, Turkki R, Walliander M, et al.

Aiforia Clinical Suite for Prostate Cancer: A Holistic AssistiveTool for Prostate Cancer Diagnostics

Karjalainen M, Tumiati M, Wester A, et al.

Evidence for the utility of artificial intelligence (AI) and image analysis in Ki-67 quantification in solid tumors

Pichon X, Gaspo R, Iglesias S, et al.

Clinical Validation and Implementation of an Artificial Intelligence Model for Digital Analysis of Ki-67 Biomarker in Breast Cancer

Rosario K, Stoy L, Blom S, et al.

Artificial Intelligence-AIded Diagnostics Saves Time and Improves Reliability of PD-L1 Scoring in Non-Small Cell Lung Canvcer

Sjöblom N, Laury A, Kovala M, et al.

Diagnosis 2.0 AI-Assisted Gleason Group Grading in Prostate Cancer

Karjalainen M, Neittaanmäki N, Puttonen H, et al.

Additional Validation of a Deep Learning Algorithm to Quantify Histologic Features in Colorectal Carcinoma

Shivji S, Pai R, Rosty C, et al.

Development of an Artificial Intelligence Model for the Evaluation of Histopathologic Features of Eosinophilic Esophagitis

Archila LR, O’Sullivan D, Cardenas-Fernandez MC, et al.

AI(H): Deep Learning Model for Staging and Grading Autoimmune Hepatitis from Histology

Ercan C, Kordy K, Knuuttila A, et al.

Artificial Intelligence Enables Efficient and Reproducible Quantitative Rodent Toxicology Ovarian Follicle Evaluation

Rudmann DG, Swanson C, Tarkiainen K, et al.

A Deep Learning Algorithm to Quantify Liver Fat Content in Humans

Qadri S, Ahtiainen L, Boyd S, et al.

Systemically administered Aflibercept protects against the development of neovascular lesions in the mouse laser-induced choroidal neovascularization model

Vähätupa M, Ragauskas S, Žiniauskaite A, et al.

Using deep neural networks to count Ki-67 positive cells in neuroendocrine tumors

Mola N & Leh S

Deep convolutional neural network-based method for quantification of the pancreatic β-cell mass in mice

Danilova T, Blom S, Ropponen T, et al.

Implementation of Neural Networks for studies of brain pathology in Parkinson´s disease

Parkkinen I, Penttinen AM, Kopra J, et al.

Deep-learning neural network in prostate cancer detection and grading

Sandeman K, Blom S, Ropponen R, et al.

A deep-learning algorithm to determine liver fat content in non-alcoholic fatty liver disease in humans