{"entity": "researcher", "timestamp": "2026-10-18T00:33:05.333Z", "family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "affiliations": ["Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. henrik.olsson@ki.se."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f"}}, "publications": [{"entity": "publication", "iuid": "f905ca88a80f42109bb997cd17743470", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f905ca88a80f42109bb997cd17743470.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f905ca88a80f42109bb997cd17743470"}}, "title": "Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol.", "authors": [{"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Blilie", "given": "Anders", "initials": "A", "orcid": "0009-0002-9546-5443", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d778d06e206948e5ac99017ce1ae8434.json"}}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Szolnoky", "given": "Kelvin", "initials": "K", "orcid": "0000-0002-0554-1872", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5059c9f881d4434f9b64f3bb381478fe.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Titus", "given": "Matteo", "initials": "M", "orcid": "0009-0008-7893-7385", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/049f236217f147f78635b9d3a361db98.json"}}, {"family": "Martinez Gonzalez", "given": "Geraldine", "initials": "G"}, {"family": "Boman", "given": "Sol Erika", "initials": "SE", "orcid": "0009-0001-5733-3178", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8a605b0dadad4b3696396827ebac9348.json"}}, {"family": "Valkonen", "given": "Masi", "initials": "M", "orcid": "0000-0003-3091-2484", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ae4841e8a4124dc79c921b3af09096fe.json"}}, {"family": "Gudlaugsson", "given": "Einar", "initials": "E"}, {"family": "Kjosavik", "given": "Svein Reidar", "initials": "SR", "orcid": "0000-0002-7004-5533", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/97167c1ee44443108098eb823bd18fa7.json"}}, {"family": "Asenjo", "given": "Jos\u00e9", "initials": "J"}, {"family": "Gambacorta", "given": "Marcello", "initials": "M"}, {"family": "Libretti", "given": "Paolo", "initials": "P"}, {"family": "Braun", "given": "Marcin", "initials": "M"}, {"family": "Kordek", "given": "Radzislaw", "initials": "R"}, {"family": "\u0141owicki", "given": "Roman", "initials": "R"}, {"family": "Hotakainen", "given": "Kristina", "initials": "K"}, {"family": "V\u00e4re", "given": "P\u00e4ivi", "initials": "P"}, {"family": "Pedersen", "given": "Bodil Ginnerup", "initials": "BG", "orcid": "0000-0003-2792-7343", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/926d2e6e88a049f8ba5e50dbdd4fe210.json"}}, {"family": "S\u00f8rensen", "given": "Karina Dalsgaard", "initials": "KD"}, {"family": "Ulh\u00f8i", "given": "Benedicte Parm", "initials": "BP"}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Tsuzuki", "given": "Toyonori", "initials": "T", "orcid": "0000-0002-4855-4366", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/42c993c2c0ab4b50afa03134f70118d9.json"}}, {"family": "Janssen", "given": "Emilius Adrianus Maria", "initials": "EAM"}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}], "type": "journal article", "published": "2025-07-07", "journal": {"title": "BMJ Open", "issn": "2044-6055", "volume": "15", "issue": "7", "pages": "e097591", "issn-l": "2044-6055"}, "abstract": "Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, risking inappropriate treatment. Similar challenges complicate the assessment of other prognostic features like cribriform cancer morphology and perineural invasion. Many pathology departments are also facing an increasingly unsustainable workload due to rising prostate cancer incidence and a decreasing pathologist workforce coinciding with increasing requirements for more complex assessments and reporting. Digital pathology and artificial intelligence (AI) algorithms for analysing whole slide images show promise in improving the accuracy and efficiency of histopathological assessments. Studies have demonstrated AI's capability to diagnose and grade prostate cancer comparably to expert pathologists. However, external validations on diverse data sets have been limited and often show reduced performance. Historically, there have been no well-established guidelines for AI study designs and validation methods. Diagnostic assessments of AI systems often lack preregistered protocols and rigorous external cohort sampling, essential for reliable evidence of their safety and accuracy.\n\nThis study protocol covers the retrospective validation of an AI system for prostate biopsy assessment. The primary objective of the study is to develop a high-performing and robust AI model for diagnosis and Gleason scoring of prostate cancer in core needle biopsies, and at scale evaluate whether it can generalise to fully external data from independent patients, pathology laboratories and digitalisation platforms. The secondary objectives cover AI performance in estimating cancer extent and detecting cribriform prostate cancer and perineural invasion. This protocol outlines the steps for data collection, predefined partitioning of data cohorts for AI model training and validation, model development and predetermined statistical analyses, ensuring systematic development and comprehensive validation of the system. The protocol adheres to Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis+AI (TRIPOD+AI), Protocol Items for External Cohort Evaluation of a Deep Learning System in Cancer Diagnostics (PIECES), Checklist for AI in Medical Imaging (CLAIM) and other relevant best practices.\n\nData collection and usage were approved by the respective ethical review boards of each participating clinical laboratory, and centralised anonymised data handling was approved by the Swedish Ethical Review Authority. The study will be conducted in agreement with the Helsinki Declaration. The findings will be disseminated in peer-reviewed publications (open access).", "doi": "10.1136/bmjopen-2024-097591", "pmid": "40623883", "labels": {"DDLS Fellow": "", "Kimmo Kartasalo": ""}, "xrefs": [{"db": "pmc", "key": "PMC12258300"}, {"db": "pii", "key": "bmjopen-2024-097591"}], "notes": [], "created": "2025-10-30T15:40:42.132Z", "modified": "2026-09-23T11:28:50.741Z"}, {"entity": "publication", "iuid": "34dd4118753048eab5325f80ccac79e7", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/34dd4118753048eab5325f80ccac79e7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/34dd4118753048eab5325f80ccac79e7"}}, "title": "Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.", "authors": [{"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Capuccini", "given": "Marco", "initials": "M"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Delahunt", "given": "Brett", "initials": "B", "orcid": "0000-0002-5398-0300", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/433a3f884f95451383cc746925d9a08c.json"}}, {"family": "Lindskog", "given": "Cecilia", "initials": "C", "orcid": "0000-0001-5611-1015", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/21610a810a87459c962d6da3f2ad38ad.json"}}, {"family": "Janssen", "given": "Emiel A M", "initials": "EAM"}, {"family": "Blilie", "given": "Anders", "initials": "A"}, {"family": "ISUP Prostate Imagebase Expert Panel", "given": "", "initials": ""}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2c192389f99d4801b91f3350e07dfb9e.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}], "type": "journal article", "published": "2022-12-15", "journal": {"title": "Nat Commun", "issn": "2041-1723", "issn-l": "2041-1723", "volume": "13", "issue": "1", "pages": "7761"}, "abstract": "Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We demonstrate the use of conformal prediction to detect unreliable predictions, using histopathological diagnosis and grading of prostate biopsies as example. We digitized 7788 prostate biopsies from 1192 men in the STHLM3 diagnostic study, used for training, and 3059 biopsies from 676 men used for testing. With conformal prediction, 1 in 794 (0.1%) predictions is incorrect for cancer diagnosis (compared to 14 errors [2%] without conformal prediction) while 175 (22%) of the predictions are flagged as unreliable when the AI-system is presented with new data from the same lab and scanner that it was trained on. Conformal prediction could with small samples (N = 49 for external scanner, N = 10 for external lab and scanner, and N = 12 for external lab, scanner and pathology assessment) detect systematic differences in external data leading to worse predictive performance. The AI-system with conformal prediction commits 3 (2%) errors for cancer detection in cases of atypical prostate tissue compared to 44 (25%) without conformal prediction, while the system flags 143 (80%) unreliable predictions. We conclude that conformal prediction can increase patient safety of AI-systems.", "doi": "10.1038/s41467-022-34945-8", "pmid": "36522311", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC9755280"}, {"db": "pii", "key": "10.1038/s41467-022-34945-8"}], "notes": [], "created": "2024-11-05T16:10:20.350Z", "modified": "2024-11-29T09:28:59.239Z"}, {"entity": "publication", "iuid": "9900bd886963425bb691338c471bedf9", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9900bd886963425bb691338c471bedf9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9900bd886963425bb691338c471bedf9"}}, "title": "OpenPhi: an interface to access Philips iSyntax whole slide images for computational pathology.", "authors": [{"family": "Mulliqi", "given": "Nita", "initials": "N"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Ji", "given": "Xiaoyi", "initials": "X"}, {"family": "Egevad", "given": "Lars", "initials": "L", "orcid": "0000-0001-8531-222X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7d46f1f5fc047dc8edb910eb4f0645c.json"}}, {"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P", "orcid": "0000-0001-9086-9591", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bb947cd395e84612a53569f4a39abd19.json"}}], "type": "journal article", "published": "2021-11-05", "journal": {"title": "Bioinformatics", "issn": "1367-4811", "issn-l": "1367-4803", "volume": "37", "issue": "21", "pages": "3995-3997"}, "abstract": "Digital pathology enables applying computational methods, such as deep learning, in pathology for improved diagnostics and prognostics, but lack of interoperability between whole slide image formats of different scanner vendors is a challenge for algorithm developers. We present OpenPhi-Open PatHology Interface, an Application Programming Interface for seamless access to the iSyntax format used by the Philips Ultra Fast Scanner, the first digital pathology scanner approved by the United States Food and Drug Administration. OpenPhi is extensible and easily interfaced with existing vendor-neutral applications.\r\n\r\nOpenPhi is implemented in Python and is available as open-source under the MIT license at: https://gitlab.com/BioimageInformaticsGroup/openphi. The Philips Software Development Kit is required and available at: https://www.openpathology.philips.com. OpenPhi version 1.1.1 is additionally provided as Supplementary Data.\r\n\r\nSupplementary data are available at Bioinformatics online.", "doi": "10.1093/bioinformatics/btab578", "pmid": "34358287", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC8570784"}, {"db": "pii", "key": "6343446"}], "notes": [], "created": "2024-11-05T16:08:25.508Z", "modified": "2024-11-29T09:30:27.734Z"}, {"entity": "publication", "iuid": "f89ec060f7f34be7ab0850d93dd84380", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f89ec060f7f34be7ab0850d93dd84380.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f89ec060f7f34be7ab0850d93dd84380"}}, "title": "The importance of study design in the application of artificial intelligence methods in medicine.", "authors": [{"family": "Eklund", "given": "Martin", "initials": "M", "orcid": "0000-0001-5032-5266", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/06721510ebc8462386e0e6fc6c84315b.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7c6fc97c06ed456c8bdd1f03db8a9b72.json"}}, {"family": "Olsson", "given": "Henrik", "initials": "H", "orcid": "0000-0002-2270-2017", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/da0547e978264d9c88fc6a222dcbfd5f.json"}}, {"family": "Str\u00f6m", "given": "Peter", "initials": "P", "orcid": "0000-0002-1631-806X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2798ea6ef2ed4ae88b78dd04d2f14437.json"}}], "type": "journal article", "published": "2019-10-18", "journal": {"title": "NPJ Digit Med", "issn": "2398-6352", "issn-l": null, "volume": "2", "issue": null, "pages": "101"}, "abstract": null, "doi": "10.1038/s41746-019-0174-1", "pmid": "31646183", "labels": {"Kimmo Kartasalo": null, "DDLS Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC6802122"}, {"db": "pii", "key": "174"}], "notes": [], "created": "2024-11-05T16:10:25.356Z", "modified": "2024-11-29T09:31:22.619Z"}]}