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Verona J, Gülsen Y, Zaninotto M, Munsaka S, Serdarevic N, Datta SK, Wiencek J, Fink N. Ethical Checklists for Clinical Research Projects and laboratory medicine: two tools to evaluate compliance with bioethical principles in different settings. Clin Chem Lab Med 2024; 0:cclm-2024-0604. [PMID: 38881198 DOI: 10.1515/cclm-2024-0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 05/30/2024] [Indexed: 06/18/2024]
Abstract
OBJECTIVES To develop two ethical checklists to evaluate (i) management of ethical concerns in research projects and (ii) awareness of ethical conduct of healthcare laboratory professionals. METHODS Comprehensive discussion among the members of IFCC Task Force on Ethics based on pertinent literature. RESULTS This Checklist for Clinical Research Projects should be useful to evaluate research proposals from an ethical perspective before submitting it to an IRB or its equivalent, thereby diminishing rejection rates and resulting in more time-effective projects. The checklist designed to evaluate the ethical conduct in laboratory medicine could be useful for self evaluation (internal audits) and for certification/accreditation processes performed by third parties. CONCLUSIONS These checklists are simple but powerful tools useful to guide professionals to adhere to ethical principles in their practice, especially in developing countries where accredited ethics committees may be difficult to find.
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Affiliation(s)
- Julián Verona
- Hospital de Balcarce "Dr. Felipe A. Fossati", Balcarce, Buenos Aires, Argentina
| | - Yilmaz Gülsen
- Department of Medical Biochemistry, Ankara Yildirim Beyazit University School of Medicine, Ankara, Türkiye
- Medical Biochemistry Laboratory, Ankara Bilkent City Hospital, Ankara, Türkiye
| | - Martina Zaninotto
- Department of Laboratory Medicine, University Hospital, Padova, Italy
| | - Sody Munsaka
- Department of Biomedical Sciences, School of Health Sciences, University of Zambia, Lusaka, Zambia
| | - Nafija Serdarevic
- Institute for Biochemistry, University of Sarajevo Clinics Center, Sarajevo, Bosnia and Herzegovina
- Faculty of Health Sciences, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Sudip K Datta
- Department of Laboratory Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Joesph Wiencek
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Nilda Fink
- Fundación Bioquímica Argentina, Programa PROES, Ciudad Autónoma de Buenos Aires, Argentina
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Fan BE, Yong BSJ, Li R, Wang SSY, Aw MYN, Chia MF, Chen DTY, Neo YS, Occhipinti B, Ling RR, Ramanathan K, Ong YX, Lim KGE, Wong WYK, Lim SP, Latiff STBA, Shanmugam H, Wong MS, Ponnudurai K, Winkler S. From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film. Blood Rev 2024; 64:101144. [PMID: 38016837 DOI: 10.1016/j.blre.2023.101144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.
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Affiliation(s)
- Bingwen Eugene Fan
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Bryan Song Jun Yong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ruiqi Li
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | | | - Ming Fang Chia
- Department of Haematology, Tan Tock Seng Hospital, Singapore
| | | | - Yuan Shan Neo
- ASUS Intelligent Cloud Services, Singapore, Singapore
| | | | - Ryan Ruiyang Ling
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kollengode Ramanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore, Singapore
| | - Yi Xiong Ong
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Shu Ping Lim
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Moh Sim Wong
- Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kuperan Ponnudurai
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stefan Winkler
- ASUS Intelligent Cloud Services, Singapore, Singapore; School of Computing, National University of Singapore, Singapore
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Singla N, Kundu R, Dey P. Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology. Cytopathology 2024; 35:226-234. [PMID: 37970960 DOI: 10.1111/cyt.13336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
Artificial Intelligence (AI) is an emerging, transforming and revolutionary technology that has captured attention worldwide. It is translating research into precision oncology treatments. AI can analyse large or big data sets requiring high-speed specialized computing solutions. The data are big in terms of volume and multimodal with the amalgamation of images, text and structure. Machine learning has identified antifungal drug targets, and taxonomic and phylogenetic classification of fungi based on sequence analysis is now available. Real-time identification tools and user-friendly mobile applications for identifying fungi have been discovered. Akin to histopathology, AI can be applied to fungal cytology. AI has been fruitful in cytopathology of the thyroid gland, breast, urine and uterine cervical lesions. AI has a huge scope in fungal cytology and would certainly bear fruit with its accuracy, reproducibility and capacity for handling big data. The purpose of this systematic review was to highlight the AI's utility in detecting fungus and its typing with a special focus on future application in fungal cytology. We also touch upon the basics of AI in brief.
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Affiliation(s)
- Nidhi Singla
- Department of Microbiology, Government Medical College and Hospital, Chandigarh, India
| | - Reetu Kundu
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Pranab Dey
- Department of Cytology and Gynaecological Pathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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Devis L, Catry E, Honore PM, Mansour A, Lippi G, Mullier F, Closset M. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Ann Intensive Care 2024; 14:9. [PMID: 38224401 PMCID: PMC10789714 DOI: 10.1186/s13613-024-01244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.
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Affiliation(s)
- Luigi Devis
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Emilie Catry
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Patrick M Honore
- Department of Intensive Care, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Alexandre Mansour
- Department of Anesthesia and Critical Care, Pontchaillou University Hospital of Rennes, Rennes, France
- IRSET-INSERM-1085, Univ Rennes, Rennes, France
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - François Mullier
- Department of Laboratory Medicine, Hematology, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Namur Thrombosis and Hemostasis Center (NTHC), Namur Research Institute for Life Sciences (NARILIS), Namur, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Mélanie Closset
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium.
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium.
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Evans H, Snead D. Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them? Histopathology 2024; 84:279-287. [PMID: 37921030 DOI: 10.1111/his.15071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 11/04/2023]
Abstract
Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.
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Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK
- Warwick Medical School, University of Warwick, Coventry, UK
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6
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Raciti P, Sue J, Retamero JA, Ceballos R, Godrich R, Kunz JD, Casson A, Thiagarajan D, Ebrahimzadeh Z, Viret J, Lee D, Schüffler PJ, DeMuth G, Gulturk E, Kanan C, Rothrock B, Reis-Filho J, Klimstra DS, Reuter V, Fuchs TJ. Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection. Arch Pathol Lab Med 2023; 147:1178-1185. [PMID: 36538386 DOI: 10.5858/arpa.2022-0066-oa] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2022] [Indexed: 09/29/2023]
Abstract
CONTEXT.— Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.— To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.— Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.— Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.— This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.
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Affiliation(s)
- Patricia Raciti
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jillian Sue
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Juan A Retamero
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Rodrigo Ceballos
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Ran Godrich
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jeremy D Kunz
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Adam Casson
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Dilip Thiagarajan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Zahra Ebrahimzadeh
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Julian Viret
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Donghun Lee
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Peter J Schüffler
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | | | - Emre Gulturk
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Christopher Kanan
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Brandon Rothrock
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Jorge Reis-Filho
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - David S Klimstra
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
| | - Victor Reuter
- The Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York (Reis-Filho, Reuter)
| | - Thomas J Fuchs
- From Paige (Raciti, Sue, Retamero, Ceballos, Godrich, Kunz, Casson, Thiagarajan, Ebrahimzadeh, Viret, Lee, Schüffler, Gulturk, Kanan, Rothrock, Klimstra, Fuchs), New York, New York
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Levy JJ, Chan N, Marotti JD, Kerr DA, Gutmann EJ, Glass RE, Dodge CP, Suriawinata AA, Christensen BC, Liu X, Vaickus LJ. Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X. Cancer Cytopathol 2023; 131:637-654. [PMID: 37377320 DOI: 10.1002/cncy.22732] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting. METHODS In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytology specimens. RESULTS The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters. CONCLUSIONS The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials.
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Affiliation(s)
- Joshua J Levy
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Department of Dermatology, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Natt Chan
- Program in Quantitative Biomedical Sciences, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Jonathan D Marotti
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Darcy A Kerr
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Edward J Gutmann
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Ryan E Glass
- Department of Pathology, University of Pittsburgh Medical Center East, Pittsburgh, Pennsylvania, USA
| | | | - Arief A Suriawinata
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Brock C Christensen
- Department of Epidemiology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Department of Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Department of Community and Family Medicine, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Xiaoying Liu
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA
- Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
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8
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Nakagawa K, Moukheiber L, Celi LA, Patel M, Mahmood F, Gondim D, Hogarth M, Levenson R. AI in Pathology: What could possibly go wrong? Semin Diagn Pathol 2023; 40:100-108. [PMID: 36882343 DOI: 10.1053/j.semdp.2023.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/25/2023] [Accepted: 02/26/2023] [Indexed: 03/05/2023]
Abstract
The field of medicine is undergoing rapid digital transformation. Pathologists are now striving to digitize their data, workflows, and interpretations, assisted by the enabling development of whole-slide imaging. Going digital means that the analog process of human diagnosis can be augmented or even replaced by rapidly evolving AI approaches, which are just now entering into clinical practice. But with such progress comes challenges that reflect a variety of stressors, including the impact of unrepresentative training data with accompanying implicit bias, data privacy concerns, and fragility of algorithm performance. Beyond such core digital aspects, considerations arise related to difficulties presented by changing disease presentations, diagnostic approaches, and therapeutic options. While some tools such as data federation can help with broadening data diversity while preserving expertise and local control, they may not be the full answer to some of these issues. The impact of AI in pathology on the field's human practitioners is still very much unknown: installation of unconscious bias and deference to AI guidance need to be understood and addressed. If AI is widely adopted, it may remove many inefficiencies in daily practice and compensate for staff shortages. It may also cause practitioner deskilling, dethrilling, and burnout. We discuss the technological, clinical, legal, and sociological factors that will influence the adoption of AI in pathology, and its eventual impact for good or ill.
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Affiliation(s)
| | | | - Leo A Celi
- Massachusetts Institute of Technology, Cambridge, MA
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9
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Bunch DR, Durant TJ, Rudolf JW. Artificial Intelligence Applications in Clinical Chemistry. Clin Lab Med 2023; 43:47-69. [PMID: 36764808 DOI: 10.1016/j.cll.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.
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Affiliation(s)
- Dustin R Bunch
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, 700 Children's Drive, C1923, Columbus, OH 43205-2644, USA; Department of Pathology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Thomas Js Durant
- Department of Laboratory Medicine, Yale School of Medicine, 55 Park Street, Room PS 502A, New Haven, CT 06510, USA
| | - Joseph W Rudolf
- Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA; ARUP Laboratories, 500 Chipeta Way, MC 115, Salt Lake City, UT 84108, USA.
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10
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Lin E, Fuda F, Luu HS, Cox AM, Fang F, Feng J, Chen M. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology. Semin Diagn Pathol 2023; 40:88-94. [PMID: 36801182 DOI: 10.1053/j.semdp.2023.02.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/04/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.
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Affiliation(s)
- Elisa Lin
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Hung S Luu
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Andrew M Cox
- Cell & Molecular Biology
- Luda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Fengqi Fang
- Department of Oncology, The First Hospital of Dalian Medical University, Dalian, China
| | - Junlin Feng
- Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, China
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America.
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11
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Queraltó J, Brady J, Carobene A, Homšak E, Wieringa G. The European Register of Specialists in Clinical Chemistry and Laboratory Medicine: code of conduct, version 3 - 2023. Clin Chem Lab Med 2023; 61:981-988. [PMID: 36724108 DOI: 10.1515/cclm-2023-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 02/02/2023]
Abstract
Whilst version 2 focussed on the professional conduct expected of a Specialist in Laboratory Medicine, version 3 builds on the responsibilities for ethical conduct from point of planning to point of care. Particular responsibilities that are outlined include: - The need for evidence when planning a new service, providing assurance that a new test does not do harm - Maintaining respect for patient confidentiality, their religious/ethnic beliefs, the need for informed consent to test, agreement on retrospective use of samples as part of governance envelopes in the pre-analytical phase - Ensuring respect for patient autonomy in the response to untoward results generated in the analytical phase - Supporting the safety of patients in the post-analytical phase through knowledge-based interpretation and presentation of results - The duty of candour to disclose and respond to error across the total testing process - Leading initiatives to harmonise and standardise pre-analytical, analytical and post-analytical phases to ensure more consistent clinical decision making with utilisation of demand management to ensure more equitable access to scarce resources - Working with emerging healthcare providers beyond the laboratory to ensure consistent application of high standards of clinical care In identifying opportunities for wider contributions to resolving ethical challenges across healthcare the need is also highlighted for more external quality assurance schemes and ethics-based quality indicators that span the total testing process.
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Affiliation(s)
- Josep Queraltó
- SEQCML - The Spanish Society of Laboratory Medicine SEQCML Barcelona, Spain
| | - Jennifer Brady
- Department of Paediatric Laboratory Medicine, UCD School of Medicine, Children's Health Ireland (CHI) Dublin, Ireland
| | - Anna Carobene
- Laboratory Medicine Department, IRCCS San Raffaele Hospital Milan, Italy
| | - Evgenija Homšak
- European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Milan, Italy
| | - Gijsbert Wieringa
- European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Milan, Italy
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12
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Pennestrì F, Banfi G. Artificial intelligence in laboratory medicine: fundamental ethical issues and normative key-points. Clin Chem Lab Med 2022; 60:1867-1874. [PMID: 35413163 DOI: 10.1515/cclm-2022-0096] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022]
Abstract
The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned.
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Affiliation(s)
| | - Giuseppe Banfi
- IRCCS Istituto Ortopedico Galeazzi, Milan, Lombardia, Italy.,Università Vita-Salute San Raffaele, Milan, Italy
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13
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Coulter C, McKay F, Hallowell N, Browning L, Colling R, Macklin P, Sorell T, Aslam M, Bryson G, Treanor D, Verrill C. Understanding the ethical and legal considerations of Digital Pathology. JOURNAL OF PATHOLOGY CLINICAL RESEARCH 2022; 8:101-115. [PMID: 34796679 PMCID: PMC8822384 DOI: 10.1002/cjp2.251] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/12/2021] [Accepted: 10/12/2021] [Indexed: 12/21/2022]
Abstract
Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.
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Affiliation(s)
- Cheryl Coulter
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Division of Clinical Laboratory Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Francis McKay
- The Wellcome Centre for Ethics and Humanities and the Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nina Hallowell
- The Wellcome Centre for Ethics and Humanities and the Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Philip Macklin
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tom Sorell
- Department of Politics and International Studies, University of Warwick, Coventry, UK
| | - Muhammad Aslam
- Department of Histopathology, Glangwilli Hospital, Hywel Dda University Health Board, Carmarthen, Wales, UK
| | - Gareth Bryson
- Department of Pathology, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, Scotland, UK
| | - Darren Treanor
- Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
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Chauhan C, Gullapalli RR. Ethics of AI in Pathology: Current Paradigms and Emerging Issues. THE AMERICAN JOURNAL OF PATHOLOGY 2021; 191:1673-1683. [PMID: 34252382 PMCID: PMC8485059 DOI: 10.1016/j.ajpath.2021.06.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/18/2021] [Accepted: 06/24/2021] [Indexed: 02/06/2023]
Abstract
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.
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Affiliation(s)
- Chhavi Chauhan
- American Society of Investigative Pathology, Rockville, Maryland
| | - Rama R Gullapalli
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico; Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico.
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