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Coudry RA, Assis EA, Frassetto FP, Jansen AM, da Silva LM, Parra-Medina R, Saieg M. Crossing the Andes: Challenges and opportunities for digital pathology in Latin America. J Pathol Inform 2024; 15:100369. [PMID: 38638195 PMCID: PMC11025004 DOI: 10.1016/j.jpi.2024.100369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 04/20/2024] Open
Abstract
The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.
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Affiliation(s)
| | | | | | | | | | - Rafael Parra-Medina
- National Cancer Institute (INC), Bogotá, Colombia
- Fundación Universitaria de Ciencias de la Salud (FUCS), Bogotá, Colombia
| | - Mauro Saieg
- Grupo Fleury, São Paulo, Brazil
- Santa Casa Medical School, São Paulo, SP, Brazil
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2
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Reyes Gil M, Pantanowitz J, Rashidi HH. Venous thromboembolism in the era of machine learning and artificial intelligence in medicine. Thromb Res 2024; 242:109121. [PMID: 39213896 DOI: 10.1016/j.thromres.2024.109121] [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: 05/06/2024] [Revised: 07/19/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care.
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Affiliation(s)
- Morayma Reyes Gil
- Thrombosis and Hemostasis Labs, Robert J. Tomsich Pathology & Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, United States of America.
| | - Joshua Pantanowitz
- Computational Pathology and AI Center of Excellence, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America
| | - Hooman H Rashidi
- Computational Pathology and AI Center of Excellence, University of Pittsburgh Medical Center, Pittsburgh, PA, United States of America.
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3
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Laohawetwanit T, Gonzalez RS, Bychkov A. Learning at a distance: results of an international survey on the adoption of virtual conferences and whole slide imaging by pathologists. J Clin Pathol 2024; 77:632-638. [PMID: 37208158 DOI: 10.1136/jcp-2023-208912] [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: 04/05/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023]
Abstract
AIMS This study presents the findings of a global survey of pathologists' views of online conferences and digital pathology. METHODS An online anonymous survey consisting of 11 questions focusing on pathologists' perceptions of virtual conferences and digital slides was distributed to practising pathologists and trainees across the globe using the authors' social media accounts and professional society connections. Participants were asked to rank their preference for various aspects of pathology meetings on a 5-point Likert scale. RESULTS There were 562 respondents from 79 countries. Several advantages of virtual meetings were recognised, including that they are less expensive to attend than in-person meetings (mean 4.4), more convenient to attend remotely (mean 4.3) and more efficient due to no loss of time for travel (mean 4.3). The lack of networking was reported as the main disadvantage of virtual conferences (mean 4.0). Most respondents (n=450, 80.1%) preferred hybrid or virtual meetings. About two-thirds (n=356, 63.3%) had no concern regarding the use of virtual slides for educational purposes and viewed them as an acceptable substitute for glass slides. CONCLUSIONS Online meetings and whole slide imaging are viewed as valuable tools in pathology education. Virtual conferences allow affordable registration fees and flexibility for participants. However, networking opportunities are limited, meaning in-person meetings cannot be entirely replaced by virtual conferences. Hybrid meetings may be a solution to maximise the benefits of both virtual and in-person meetings.
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Affiliation(s)
- Thiyaphat Laohawetwanit
- Division of Pathology, Chulabhorn International College of Medicine, Thammasat University, Pathum Thani, Thailand
| | - Raul S Gonzalez
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, USA
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
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Moodley Y. The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Interstitial Lung Disease. Am J Respir Crit Care Med 2024; 210:378-380. [PMID: 38593003 PMCID: PMC11351808 DOI: 10.1164/rccm.202403-0603ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 04/08/2024] [Indexed: 04/11/2024] Open
Affiliation(s)
- Yuben Moodley
- Faculty of Medicine University of Western Australia Perth, Western Australia, Australia
- Department of Respiratory Medicine Fiona Stanley Hospital Murdoch, Western Australia, Australia
- Institute for Respiratory Health Nedlands, Western Australia, Australia
- Centre of Research Excellence in Pulmonary Fibrosis Camperdown, New South Wales, Australia
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5
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Fuchs J, Rabaux-Eygasier L, Guerin F. Artificial Intelligence in Pediatric Liver Transplantation: Opportunities and Challenges of a New Era. CHILDREN (BASEL, SWITZERLAND) 2024; 11:996. [PMID: 39201931 PMCID: PMC11352562 DOI: 10.3390/children11080996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 08/09/2024] [Accepted: 08/13/2024] [Indexed: 09/03/2024]
Abstract
Historically, pediatric liver transplantation has achieved significant milestones, yet recent innovations have predominantly occurred in adult liver transplantation due to higher caseloads and ethical barriers in pediatric studies. Emerging methods subsumed under the term artificial intelligence offer the potential to revolutionize data analysis in pediatric liver transplantation by handling complex datasets without the need for interventional studies, making them particularly suitable for pediatric research. This review provides an overview of artificial intelligence applications in pediatric liver transplantation. Despite some promising early results, artificial intelligence is still in its infancy in the field of pediatric liver transplantation, and its clinical implementation faces several challenges. These include the need for high-quality, large-scale data and ensuring the interpretability and transparency of machine and deep learning models. Ethical considerations, such as data privacy and the potential for bias, must also be addressed. Future directions for artificial intelligence in pediatric liver transplantation include improving donor-recipient matching, managing long-term complications, and integrating diverse data sources to enhance predictive accuracy. Moreover, multicenter collaborations and prospective studies are essential for validating artificial intelligence models and ensuring their generalizability. If successfully integrated, artificial intelligence could lead to substantial improvements in patient outcomes, bringing pediatric liver transplantation again to the forefront of innovation in the transplantation community.
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Affiliation(s)
- Juri Fuchs
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, 69120 Heidelberg, Germany;
- Department of Pediatric Surgery, Université Paris-Saclay, Assistance Publique Hôpitaux de Paris (AP-HP), Bicêtre Hospital, 94270 Le Kremlin Bicêtre, France;
| | - Lucas Rabaux-Eygasier
- Department of Pediatric Surgery, Université Paris-Saclay, Assistance Publique Hôpitaux de Paris (AP-HP), Bicêtre Hospital, 94270 Le Kremlin Bicêtre, France;
| | - Florent Guerin
- Department of Pediatric Surgery, Université Paris-Saclay, Assistance Publique Hôpitaux de Paris (AP-HP), Bicêtre Hospital, 94270 Le Kremlin Bicêtre, France;
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6
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Cigliano A, Liao W, Deiana GA, Rizzo D, Chen X, Calvisi DF. Preclinical Models of Hepatocellular Carcinoma: Current Utility, Limitations, and Challenges. Biomedicines 2024; 12:1624. [PMID: 39062197 PMCID: PMC11274649 DOI: 10.3390/biomedicines12071624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/16/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024] Open
Abstract
Hepatocellular carcinoma (HCC), the predominant primary liver tumor, remains one of the most lethal cancers worldwide, despite the advances in therapy in recent years. In addition to the traditional chemically and dietary-induced HCC models, a broad spectrum of novel preclinical tools have been generated following the advent of transgenic, transposon, organoid, and in silico technologies to overcome this gloomy scenario. These models have become rapidly robust preclinical instruments to unravel the molecular pathogenesis of liver cancer and establish new therapeutic approaches against this deadly disease. The present review article aims to summarize and discuss the commonly used preclinical models for HCC, evaluating their strengths and weaknesses.
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Affiliation(s)
- Antonio Cigliano
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (A.C.); (G.A.D.); (D.R.)
| | - Weiting Liao
- Department of Bioengineering and Therapeutic Sciences and Liver Center, University of California, San Francisco, CA 94143, USA; (W.L.); (X.C.)
- Cancer Biology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Giovanni A. Deiana
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (A.C.); (G.A.D.); (D.R.)
| | - Davide Rizzo
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (A.C.); (G.A.D.); (D.R.)
| | - Xin Chen
- Department of Bioengineering and Therapeutic Sciences and Liver Center, University of California, San Francisco, CA 94143, USA; (W.L.); (X.C.)
- Cancer Biology Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Diego F. Calvisi
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (A.C.); (G.A.D.); (D.R.)
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Ihara K, Dumkrieger G, Zhang P, Takizawa T, Schwedt TJ, Chiang CC. Application of Artificial Intelligence in the Headache Field. Curr Pain Headache Rep 2024:10.1007/s11916-024-01297-5. [PMID: 38976174 DOI: 10.1007/s11916-024-01297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/27/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE OF REVIEW Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.
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Affiliation(s)
- Keiko Ihara
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
- Japanese Red Cross Ashikaga Hospital, Ashikaga, Tochigi, Japan
| | | | - Pengfei Zhang
- Department of Neurology, Rutgers University, New Brunswick, NJ, USA
| | - Tsubasa Takizawa
- Department of Neurology, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Todd J Schwedt
- Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
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Kim J, Bae S, Yoon SM, Jeong S. Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies. Front Oncol 2024; 14:1346237. [PMID: 39035745 PMCID: PMC11258423 DOI: 10.3389/fonc.2024.1346237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/31/2024] [Indexed: 07/23/2024] Open
Abstract
Pancreatic cancer is one of the most lethal cancers worldwide, with a 5-year survival rate of less than 5%, the lowest of all cancer types. Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive pancreatic cancer and has been classified as a health emergency in the past few decades. The histopathological diagnosis and prognosis evaluation of PDAC is time-consuming, laborious, and challenging in current clinical practice conditions. Pathological artificial intelligence (AI) research has been actively conducted lately. However, accessing medical data is challenging; the amount of open pathology data is small, and the absence of open-annotation data drawn by medical staff makes it difficult to conduct pathology AI research. Here, we provide easily accessible high-quality annotation data to address the abovementioned obstacles. Data evaluation is performed by supervised learning using a deep convolutional neural network structure to segment 11 annotated PDAC histopathological whole slide images (WSIs) drawn by medical staff directly from an open WSI dataset. We visualized the segmentation results of the histopathological images with a Dice score of 73% on the WSIs, including PDAC areas, thus identifying areas important for PDAC diagnosis and demonstrating high data quality. Additionally, pathologists assisted by AI can significantly increase their work efficiency. The pathological AI guidelines we propose are effective in developing histopathological AI for PDAC and are significant in the clinical field.
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Affiliation(s)
- Jongkwang Kim
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sumok Bae
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Seong-Mi Yoon
- Department of Pathology at the Medical Center, Medical Research Institute, ORTHOTECH, Daegu, Republic of Korea
| | - Sungmoon Jeong
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
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9
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Cui M, Deng F, Disis ML, Cheng C, Zhang L. Advances in the Clinical Application of High-throughput Proteomics. EXPLORATORY RESEARCH AND HYPOTHESIS IN MEDICINE 2024; 9:209-220. [PMID: 39148720 PMCID: PMC11326426 DOI: 10.14218/erhm.2024.00006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.
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Affiliation(s)
- Miao Cui
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Mount Sinai West, New York, NY, USA
| | - Fei Deng
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
| | - Mary L Disis
- UW Medicine Cancer Vaccine Institute, University of Washington, Seattle, WA, USA
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Lanjing Zhang
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA
- Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
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10
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Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep 2024; 14:14892. [PMID: 38937503 PMCID: PMC11211475 DOI: 10.1038/s41598-024-63707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh Medical Center, and University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Aamer Ikram
- National Institutes of Health, Islamabad, Pakistan
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Adnan Bashir
- Health Information Systems Program (HISP), Islamabad, Pakistan
| | | | - Amna Ali
- National Institutes of Health, Islamabad, Pakistan
| | - Hamza Tanvir
- National Institutes of Health, Islamabad, Pakistan
| | | | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Nasim Akhtar
- Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | | | - Mohammed Umer
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Naeem Akhter
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Rafi Butt
- Isolation Hospital and Infectious Treatment Centre, Islamabad, Pakistan
| | - Brandon D Fennell
- Department of Medicine, University of California, San Francisco, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA.
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Sorrentino FS, Gardini L, Fontana L, Musa M, Gabai A, Maniaci A, Lavalle S, D’Esposito F, Russo A, Longo A, Surico PL, Gagliano C, Zeppieri M. Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence. J Pers Med 2024; 14:690. [PMID: 39063944 PMCID: PMC11278069 DOI: 10.3390/jpm14070690] [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: 05/30/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. AIM This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). METHODS The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. RESULTS Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. CONCLUSION A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.
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Affiliation(s)
| | - Lorenzo Gardini
- Unit of Ophthalmology, Department of Surgical Sciences, Ospedale Maggiore, 40100 Bologna, Italy; (F.S.S.)
| | - Luigi Fontana
- Ophthalmology Unit, Department of Surgical Sciences, Alma Mater Studiorum University of Bologna, IRCCS Azienda Ospedaliero-Universitaria Bologna, 40100 Bologna, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Andrea Gabai
- Department of Ophthalmology, Humanitas-San Pio X, 20159 Milan, Italy
| | - Antonino Maniaci
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Salvatore Lavalle
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
| | - Fabiana D’Esposito
- Imperial College Ophthalmic Research Group (ICORG) Unit, Imperial College, 153-173 Marylebone Rd, London NW15QH, UK
- Department of Neurosciences, Reproductive Sciences and Dentistry, University of Naples Federico II, Via Pansini 5, 80131 Napoli, Italy
| | - Andrea Russo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Antonio Longo
- Department of Ophthalmology, University of Catania, 95123 Catania, Italy
| | - Pier Luigi Surico
- Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
| | - Caterina Gagliano
- Department of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, 33100 Udine, Italy
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12
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Englezos D, Voutouri C, Stylianopoulos T. Machine learning analysis reveals tumor stiffness and hypoperfusion as biomarkers predictive of cancer treatment efficacy. Transl Oncol 2024; 44:101944. [PMID: 38552284 PMCID: PMC10990740 DOI: 10.1016/j.tranon.2024.101944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/06/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
In the pursuit of advancing cancer therapy, this study explores the predictive power of machine learning in analyzing tumor characteristics, specifically focusing on the effects of tumor stiffness and perfusion (i.e., blood flow) on treatment efficacy. Recent advancements in oncology have highlighted the significance of these physiological properties of the tumor microenvironment in determining treatment outcomes. We delve into the relationship between these tumor attributes and the effectiveness of cancer therapies in preclinical tumor models. Utilizing robust statistical methods and machine learning algorithms, our research analyzes data from 1365 cases of various cancer types, assessing how tumor stiffness and perfusion influence the efficacy of treatment protocols. We also investigate the synergistic potential of combining drugs that modulate tumor stiffness and perfusion with standard cytotoxic treatments. By incorporating these predictors into treatment planning, our study aims to enhance the precision of cancer therapy, tailoring treatment to individual tumor profiles. Our findings demonstrate a significant correlation between stiffness/perfusion and treatment efficacy, highlighting a new way for personalized cancer treatment strategies.
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Affiliation(s)
- Demetris Englezos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Chrysovalantis Voutouri
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
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13
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Sulaieva O, Dudin O, Koshyk O, Panko M, Kobyliak N. Digital pathology implementation in cancer diagnostics: towards informed decision-making. Front Digit Health 2024; 6:1358305. [PMID: 38873358 PMCID: PMC11169727 DOI: 10.3389/fdgth.2024.1358305] [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: 12/19/2023] [Accepted: 05/16/2024] [Indexed: 06/15/2024] Open
Abstract
Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in clinical practice provides plenty of benefits but also harbors hidden ethical challenges affecting physician-patient relationships. This paper addresses the ethical obligation to transform the physician-patient relationship for informed and responsible decision-making when using artificial intelligence (AI)-based tools for cancer diagnostics. DP application allows to improve the performance of the Human-AI Team shifting focus from AI challenges towards the Augmented Human Intelligence (AHI) benefits. AHI enhances analytical sensitivity and empowers pathologists to deliver accurate diagnoses and assess predictive biomarkers for further personalized treatment of cancer patients. At the same time, patients' right to know about using AI tools, their accuracy, strengths and limitations, measures for privacy protection, acceptance of privacy concerns and legal protection defines the duty of physicians to provide the relevant information about AHI-based solutions to patients and the community for building transparency, understanding and trust, respecting patients' autonomy and empowering informed decision-making in oncology.
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Affiliation(s)
- Oksana Sulaieva
- Medical LaboratoryCSD, Kyiv, Ukraine
- Endocrinology Department, Bogomolets National Medical University, Kyiv, Ukraine
| | | | | | | | - Nazarii Kobyliak
- Medical LaboratoryCSD, Kyiv, Ukraine
- Endocrinology Department, Bogomolets National Medical University, Kyiv, Ukraine
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14
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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15
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Beaulieu-Jones BR, Berrigan MT, Shah S, Marwaha JS, Lai SL, Brat GA. Evaluating capabilities of large language models: Performance of GPT-4 on surgical knowledge assessments. Surgery 2024; 175:936-942. [PMID: 38246839 PMCID: PMC10947829 DOI: 10.1016/j.surg.2023.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/09/2023] [Accepted: 12/15/2023] [Indexed: 01/23/2024]
Abstract
BACKGROUND Artificial intelligence has the potential to dramatically alter health care by enhancing how we diagnose and treat disease. One promising artificial intelligence model is ChatGPT, a general-purpose large language model trained by OpenAI. ChatGPT has shown human-level performance on several professional and academic benchmarks. We sought to evaluate its performance on surgical knowledge questions and assess the stability of this performance on repeat queries. METHODS We evaluated the performance of ChatGPT-4 on questions from the Surgical Council on Resident Education question bank and a second commonly used surgical knowledge assessment, referred to as Data-B. Questions were entered in 2 formats: open-ended and multiple-choice. ChatGPT outputs were assessed for accuracy and insights by surgeon evaluators. We categorized reasons for model errors and the stability of performance on repeat queries. RESULTS A total of 167 Surgical Council on Resident Education and 112 Data-B questions were presented to the ChatGPT interface. ChatGPT correctly answered 71.3% and 67.9% of multiple choice and 47.9% and 66.1% of open-ended questions for Surgical Council on Resident Education and Data-B, respectively. For both open-ended and multiple-choice questions, approximately two-thirds of ChatGPT responses contained nonobvious insights. Common reasons for incorrect responses included inaccurate information in a complex question (n = 16, 36.4%), inaccurate information in a fact-based question (n = 11, 25.0%), and accurate information with circumstantial discrepancy (n = 6, 13.6%). Upon repeat query, the answer selected by ChatGPT varied for 36.4% of questions answered incorrectly on the first query; the response accuracy changed for 6/16 (37.5%) questions. CONCLUSION Consistent with findings in other academic and professional domains, we demonstrate near or above human-level performance of ChatGPT on surgical knowledge questions from 2 widely used question banks. ChatGPT performed better on multiple-choice than open-ended questions, prompting questions regarding its potential for clinical application. Unique to this study, we demonstrate inconsistency in ChatGPT responses on repeat queries. This finding warrants future consideration including efforts at training large language models to provide the safe and consistent responses required for clinical application. Despite near or above human-level performance on question banks and given these observations, it is unclear whether large language models such as ChatGPT are able to safely assist clinicians in providing care.
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Affiliation(s)
- Brendin R Beaulieu-Jones
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA. https://twitter.com/bratogram
| | | | - Sahaj Shah
- Geisinger Commonwealth School of Medicine, Scranton, PA
| | - Jayson S Marwaha
- Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Shuo-Lun Lai
- Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Gabriel A Brat
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA.
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16
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Naseri S, Shukla S, Hiwale KM, Jagtap MM, Gadkari P, Gupta K, Deshmukh M, Sagar S. From Pixels to Prognosis: A Narrative Review on Artificial Intelligence's Pioneering Role in Colorectal Carcinoma Histopathology. Cureus 2024; 16:e59171. [PMID: 38807833 PMCID: PMC11129955 DOI: 10.7759/cureus.59171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 04/27/2024] [Indexed: 05/30/2024] Open
Abstract
Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.
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Affiliation(s)
- Suhit Naseri
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Samarth Shukla
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - K M Hiwale
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Miheer M Jagtap
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Pravin Gadkari
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Kartik Gupta
- Radiation Oncology, Delhi State Cancer Institute, Delhi, IND
| | - Mamta Deshmukh
- Pathology, Indian Institute of Medical Sciences and Research, Jalna, IND
| | - Shakti Sagar
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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17
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Parmar UPS, Surico PL, Singh RB, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:527. [PMID: 38674173 PMCID: PMC11052176 DOI: 10.3390/medicina60040527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/12/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024]
Abstract
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.
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Affiliation(s)
| | - Pier Luigi Surico
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Rohan Bir Singh
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Francesco Romano
- Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA
| | - Carlo Salati
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
| | - Leopoldo Spadea
- Eye Clinic, Policlinico Umberto I, “Sapienza” University of Rome, 00142 Rome, Italy
| | - Mutali Musa
- Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria
| | - Caterina Gagliano
- Faculty of Medicine and Surgery, University of Enna “Kore”, Piazza dell’Università, 94100 Enna, Italy
- Eye Clinic, Catania University, San Marco Hospital, Viale Carlo Azeglio Ciampi, 95121 Catania, Italy
| | - Tommaso Mori
- Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Department of Ophthalmology, University of California San Diego, La Jolla, CA 92122, USA
| | - Marco Zeppieri
- Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy
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18
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Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
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Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
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19
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Eccher A, Becker JU, Pagni F, Cazzaniga G, Rossi M, Gambaro G, L’Imperio V, Marletta S. The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective. Life (Basel) 2024; 14:254. [PMID: 38398762 PMCID: PMC10890315 DOI: 10.3390/life14020254] [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: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open
Abstract
Kidney transplantation is the best treatment for end-stage renal disease since it offers the greatest survival benefit compared to dialysis. The gap between the number of renal transplants performed and the number of patients awaiting renal transplants leads to a steadily increasing pressure on the scientific community. Kidney preimplantation biopsy is used as a component of the evaluation of organ quality before acceptance for transplantation. However, the reliability and predictive value of biopsy data are controversial. Most of the previously proposed predictive models were not associated with graft survival, but what has to be reaffirmed is that histologic examination of kidney tissue can provide an objective window on the state of the organ that cannot be deduced from clinical records and renal functional studies. The balance of evidence indicates that reliable decisions about donor suitability must be made based on the overall picture. This work discusses recent trends that can reduce diagnostic timing and variability among players in the decision-making process that lead to kidney transplants, from the pathologist's perspective.
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Affiliation(s)
- Albino Eccher
- Department of Medical and Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41100 Modena, Italy
| | - Jan Ulrich Becker
- Institute of Pathology, University Hospital of Cologne, 50923 Cologne, Germany;
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Mattia Rossi
- Division of Nephrology, Department of Medicine, University of Verona, 37129 Verona, Italy; (M.R.); (G.G.)
| | - Giovanni Gambaro
- Division of Nephrology, Department of Medicine, University of Verona, 37129 Verona, Italy; (M.R.); (G.G.)
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, 20126 Milano, Italy; (F.P.); (G.C.); (V.L.)
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
- Division of Pathology, Humanitas Istituto Clinico Catanese, 95045 Catania, Italy
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20
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Fisher TB, Saini G, Rekha TS, Krishnamurthy J, Bhattarai S, Callagy G, Webber M, Janssen EAM, Kong J, Aneja R. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer. Breast Cancer Res 2024; 26:12. [PMID: 38238771 PMCID: PMC10797728 DOI: 10.1186/s13058-023-01752-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. METHODS H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. RESULTS The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. CONCLUSION Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.
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Affiliation(s)
- Timothy B Fisher
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA
| | - Geetanjali Saini
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - T S Rekha
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Jayashree Krishnamurthy
- JSSAHER (JSS Academy of Higher Education and Research) Medical College, Mysuru, Karnataka, India
| | - Shristi Bhattarai
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Grace Callagy
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Mark Webber
- Discipline of Pathology, University of Galway, Galway, Ireland
| | - Emiel A M Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway
| | - Jun Kong
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA.
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, 30302, USA.
- School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
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21
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Olcay B, Ozdemir GD, Ozdemir MA, Ercan UK, Guren O, Karaman O. Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning. BMC Biomed Eng 2024; 6:1. [PMID: 38233957 DOI: 10.1186/s42490-024-00075-z] [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: 03/24/2023] [Accepted: 01/09/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Infectious diseases not only cause severe health problems but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. Both conventional approaches, such as antimicrobial agents, and novel approaches, like antimicrobial peptides (AMPs), are used to treat infections. However, due to the drawbacks of current approaches, new solutions are still being investigated. One recent approach is the use of AMPs and antimicrobial agents in combination, but determining synergism is with a huge variety of AMPs time-consuming and requires multiple experimental studies. Machine learning (ML) algorithms are widely used to predict biological outcomes, particularly in the field of AMPs, but no previous research reported on predicting the synergistic effects of AMPs and antimicrobial agents. RESULTS Several supervised ML models were implemented to accurately predict the synergistic effect of AMPs and antimicrobial agents. The results demonstrated that the hyperparameter-optimized Light Gradient Boosted Machine Classifier (oLGBMC) yielded the best test accuracy of 76.92% for predicting the synergistic effect. Besides, the feature importance analysis reveals that the target microbial species, the minimum inhibitory concentrations (MICs) of the AMP and the antimicrobial agents, and the used antimicrobial agent were the most important features for the prediction of synergistic effect, which aligns with recent experimental studies in the literature. CONCLUSION This study reveals that ML algorithms can predict the synergistic activity of two different antimicrobial agents without the need for complex and time-consuming experimental procedures. The implications support that the ML models may not only reduce the experimental cost but also provide validation of experimental procedures.
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Affiliation(s)
- Basak Olcay
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey
| | - Gizem D Ozdemir
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Mehmet A Ozdemir
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.
| | - Utku K Ercan
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Onan Guren
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Ozan Karaman
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
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22
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Arvisais-Anhalt S, Gonias SL, Murray SG. Establishing priorities for implementation of large language models in pathology and laboratory medicine. Acad Pathol 2024; 11:100101. [PMID: 38292297 PMCID: PMC10825232 DOI: 10.1016/j.acpath.2023.100101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/02/2023] [Accepted: 10/29/2023] [Indexed: 02/01/2024] Open
Abstract
Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.
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Affiliation(s)
- Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Steven L. Gonias
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Sara G. Murray
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
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23
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Mądziel M. Instantaneous CO 2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:6944-6959. [PMID: 38155311 PMCID: PMC11294266 DOI: 10.1007/s11356-023-31022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 11/07/2023] [Indexed: 12/30/2023]
Abstract
One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.
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Affiliation(s)
- Maksymilian Mądziel
- Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959, Rzeszow, Poland.
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Ryou H, Lomas O, Theissen H, Thomas E, Rittscher J, Royston D. Quantitative interpretation of bone marrow biopsies in MPN-What's the point in a molecular age? Br J Haematol 2023; 203:523-535. [PMID: 37858962 PMCID: PMC10952168 DOI: 10.1111/bjh.19154] [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: 06/20/2023] [Revised: 09/20/2023] [Accepted: 09/30/2023] [Indexed: 10/21/2023]
Abstract
The diagnosis of myeloproliferative neoplasms (MPN) requires the integration of clinical, morphological, genetic and immunophenotypic findings. Recently, there has been a transformation in our understanding of the cellular and molecular mechanisms underlying disease initiation and progression in MPN. This has been accompanied by the widespread application of high-resolution quantitative molecular techniques. By contrast, microscopic interpretation of bone marrow biopsies by haematologists/haematopathologists remains subjective and qualitative. However, advances in tissue image analysis and artificial intelligence (AI) promise to transform haematopathology. Pioneering studies in bone marrow image analysis offer to refine our understanding of the boundaries between reactive samples and MPN subtypes and better capture the morphological correlates of high-risk disease. They also demonstrate potential to improve the evaluation of current and novel therapeutics for MPN and other blood cancers. With increased therapeutic targeting of diverse molecular, cellular and extra-cellular components of the marrow, these approaches can address the unmet need for improved objective and quantitative measures of disease modification in the context of clinical trials. This review focuses on the state-of-the-art in image analysis/AI of bone marrow tissue, with an emphasis on its potential to complement and inform future clinical studies and research in MPN.
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Affiliation(s)
- Hosuk Ryou
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Oliver Lomas
- Department of HaematologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Helen Theissen
- Department of Engineering Science, Institute of Biomedical Engineering (IBME)University of OxfordOxfordUK
| | - Emily Thomas
- Department of Engineering Science, Institute of Biomedical Engineering (IBME)University of OxfordOxfordUK
| | - Jens Rittscher
- Department of Engineering Science, Institute of Biomedical Engineering (IBME)University of OxfordOxfordUK
- Ground Truth LabsOxfordUK
- Oxford NIHR Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
- Ludwig Institute for Cancer ResearchUniversity of OxfordOxfordUK
| | - Daniel Royston
- Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Department of PathologyOxford University Hospitals NHS Foundation TrustOxfordUK
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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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Rashidi HH, Fennell BD, Albahra S, Hu B, Gorbett T. The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool. J Pathol Inform 2023; 14:100342. [PMID: 38116171 PMCID: PMC10727991 DOI: 10.1016/j.jpi.2023.100342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 12/21/2023] Open
Abstract
AI Chat Bots such as ChatGPT are revolutionizing our AI capabilities, especially in text generation, to help expedite many tasks, but they introduce new dilemmas. The detection of AI-generated text has become a subject of great debate considering the AI text detector's known and unexpected limitations. Thus far, much research in this area has focused on the detection of AI-generated text; however, the goal of this study was to evaluate the opposite scenario, an AI-text detection tool's ability to discriminate human-generated text. Thousands of abstracts from several of the most well-known scientific journals were used to test the predictive capabilities of these detection tools, assessing abstracts from 1980 to 2023. We found that the AI text detector erroneously identified up to 8% of the known real abstracts as AI-generated text. This further highlights the current limitations of such detection tools and argues for novel detectors or combined approaches that can address this shortcoming and minimize its unanticipated consequences as we navigate this new AI landscape.
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Affiliation(s)
- Hooman H. Rashidi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Brandon D. Fennell
- University of California, San Francisco – Department of Medicine, San Francisco, CA, United States
| | - Samer Albahra
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Bo Hu
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Tom Gorbett
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
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Rampogu S. A review on the use of machine learning techniques in monkeypox disease prediction. SCIENCE IN ONE HEALTH 2023; 2:100040. [PMID: 39077048 PMCID: PMC11262284 DOI: 10.1016/j.soh.2023.100040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/18/2023] [Indexed: 07/31/2024]
Abstract
Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.
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Joshua Ibidoja O, Pei Shan F, Eri Suheri M, Sulaiman J, Majahar Ali MK. Intelligence System via Machine Learning Algorithms in Detecting the Moisture Content Removal Parameters of Seaweed Big Data. PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY 2023; 31:2783-2803. [DOI: 10.47836/pjst.31.6.09] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Abstract
The parameters that determine the removal of moisture content have become necessary in seaweed research as they can reduce cost and improve the quality and quantity of the seaweed. During the seaweed’s drying process, many drying parameters are involved, so it is hard to find a model that can determine the drying parameters. This study compares seaweed big data performance using machine learning algorithms. To achieve the objectives, four machine learning algorithms, such as bagging, boosting, support vector machine, and random forest, were used to determine the significant parameters from the data obtained from v-GHSD (v-Groove Hybrid Solar Drier). The mean absolute percentage error (MAPE) and coefficient of determination (R2) were used to assess the model. The importance of variable selection cannot be overstated in big data due to the large number of variables and parameters that exceed the number of observations. It will reduce the complexity of the model, avoid the curse of dimensionality, reduce cost, remove irrelevant variables, and increase precision. A total of 435 drying parameters determined the moisture content removal, and each algorithm was used to select 15, 25, 35 and 45 significant parameters. The MAPE and R-Square for the 45 highest variable importance for random forest are 2.13 and 0.9732, respectively. It performed best, with the lowest error and the highest R-square. These results show that random forest is the best algorithm to decide the vital drying parameters for removing moisture content.
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Newgard CD, Babcock SR, Song X, Remick KE, Gausche-Hill M, Lin A, Malveau S, Mann NC, Nathens AB, Cook JNB, Jenkins PC, Burd RS, Hewes HA, Glass NE, Jensen AR, Fallat ME, Ames SG, Salvi A, McConnell KJ, Ford R, Auerbach M, Bailey J, Riddick TA, Xin H, Kuppermann N. Emergency Department Pediatric Readiness Among US Trauma Centers: A Machine Learning Analysis of Components Associated With Survival. Ann Surg 2023; 278:e580-e588. [PMID: 36538639 PMCID: PMC10149578 DOI: 10.1097/sla.0000000000005741] [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] [Indexed: 12/24/2022]
Abstract
OBJECTIVE We used machine learning to identify the highest impact components of emergency department (ED) pediatric readiness for predicting in-hospital survival among children cared for in US trauma centers. BACKGROUND ED pediatric readiness is associated with improved short-term and long-term survival among injured children and part of the national verification criteria for US trauma centers. However, the components of ED pediatric readiness most predictive of survival are unknown. METHODS This was a retrospective cohort study of injured children below 18 years treated in 458 trauma centers from January 1, 2012, through December 31, 2017, matched to the 2013 National ED Pediatric Readiness Assessment and the American Hospital Association survey. We used machine learning to analyze 265 potential predictors of survival, including 152 ED readiness variables, 29 patient variables, and 84 ED-level and hospital-level variables. The primary outcome was in-hospital survival. RESULTS There were 274,756 injured children, including 4585 (1.7%) who died. Nine ED pediatric readiness components were associated with the greatest increase in survival: policy for mental health care (+8.8% change in survival), policy for patient assessment (+7.5%), specific respiratory equipment (+7.2%), policy for reduced-dose radiation imaging (+7.0%), physician competency evaluations (+4.9%), recording weight in kilograms (+3.2%), life support courses for nursing (+1.0%-2.5%), and policy on pediatric triage (+2.5%). There was a 268% improvement in survival when the 5 highest impact components were present. CONCLUSIONS ED pediatric readiness components related to specific policies, personnel, and equipment were the strongest predictors of pediatric survival and worked synergistically when combined.
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Affiliation(s)
- Craig D. Newgard
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Sean R. Babcock
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Xubo Song
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Katherine E. Remick
- Departments of Pediatrics and Surgery, Dell Medical School, University of Texas at Austin, Austin, Texas
| | - Marianne Gausche-Hill
- Los Angeles County Emergency Medical Services, Harbor-UCLA Medical Center, Torrance, California
| | - Amber Lin
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Susan Malveau
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - N. Clay Mann
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Avery B. Nathens
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Jennifer N. B. Cook
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Peter C. Jenkins
- Department of Surgery, Indiana University School of Medicine, Indianapolis, Indiana
| | - Randall S. Burd
- Division of Trauma and Burn Surgery, Center for Surgical Care, Children’s National Hospital, Washington, District of Columbia
| | - Hilary A. Hewes
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Nina E. Glass
- Department of Surgery, Rutgers New Jersey Medical School, Newark, New Jersey
| | - Aaron R. Jensen
- Department of Surgery, University of California, San Francisco, Benioff Children’s Hospitals, San Francisco, California
| | - Mary E. Fallat
- Department of Surgery, University of Louisville School of Medicine, Norton Children’s Hospital, Louisville, Kentucky
| | - Stefanie G. Ames
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah
| | - Apoorva Salvi
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - K. John McConnell
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
- Center for Health Systems Effectiveness, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Rachel Ford
- Oregon Emergency Medical Services for Children Program, Oregon Health Authority, Portland, Oregon
| | - Marc Auerbach
- Departments of Pediatrics and Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Jessica Bailey
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Tyne A. Riddick
- Oregon Health & Science University-Portland State University, School of Public Health, Portland, Oregon
| | - Haichang Xin
- Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, Oregon
| | - Nathan Kuppermann
- Departments of Emergency Medicine and Pediatrics, University of California, Davis School of Medicine, Sacramento, California
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Najjar R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics (Basel) 2023; 13:2760. [PMID: 37685300 PMCID: PMC10487271 DOI: 10.3390/diagnostics13172760] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/01/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023] Open
Abstract
This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.
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Affiliation(s)
- Reabal Najjar
- Canberra Health Services, Australian Capital Territory 2605, Australia
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Freiberg J, Welikala RA, Rovelt J, Owen CG, Rudnicka AR, Kolko M, Barman SA. Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting. PLoS One 2023; 18:e0290278. [PMID: 37616264 PMCID: PMC10449151 DOI: 10.1371/journal.pone.0290278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/29/2023] [Indexed: 08/26/2023] Open
Abstract
PURPOSE To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. METHOD The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. RESULTS QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. CONCLUSION QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.
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Affiliation(s)
- Josefine Freiberg
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Roshan A. Welikala
- School of Computer Science and Mathematics, Kingston University, Surrey, United Kingdom
| | - Jens Rovelt
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Christopher G. Owen
- Population Health Research Institute, St. George’s, University of London, London, United Kingdom
| | - Alicja R. Rudnicka
- Population Health Research Institute, St. George’s, University of London, London, United Kingdom
| | - Miriam Kolko
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Ophthalmology, Copenhagen University Hospital, Rigshospitalet, Glostrup, Copenhagen, Denmark
| | - Sarah A. Barman
- School of Computer Science and Mathematics, Kingston University, Surrey, United Kingdom
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Fisher TB, Saini G, Ts R, Krishnamurthy J, Bhattarai S, Callagy G, Webber M, Janssen EAM, Kong J, Aneja R. Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer. RESEARCH SQUARE 2023:rs.3.rs-3243195. [PMID: 37645881 PMCID: PMC10462230 DOI: 10.21203/rs.3.rs-3243195/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Background Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. Methods H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. Results The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. Conclusion Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.
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Affiliation(s)
| | | | - Rekha Ts
- JSSAHER (JSS Academy of Higher Education and Research) Medical College
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Koch Nogueira PC, Venson AH, de Carvalho MFC, Konstantyner T, Sesso R. Symptoms for early diagnosis of chronic kidney disease in children - a machine learning-based score. Eur J Pediatr 2023; 182:3631-3637. [PMID: 37233777 DOI: 10.1007/s00431-023-05032-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 05/27/2023]
Abstract
The objective of this study was to reveal the signs and symptoms for the classification of pediatric patients at risk of CKD using decision trees and extreme gradient boost models for predicting outcomes. A case-control study was carried out involving children with 376 chronic kidney disease (cases) and a control group of healthy children (n = 376). A family member responsible for the children answered a questionnaire with variables potentially associated with the disease. Decision tree and extreme gradient boost models were developed to test signs and symptoms for the classification of children. As a result, the decision tree model revealed 6 variables associated with CKD, whereas twelve variables that distinguish CKD from healthy children were found in the "XGBoost". The accuracy of the "XGBoost" model (ROC AUC = 0.939, 95%CI: 0.911 to 0.977) was the highest, while the decision tree model was a little lower (ROC AUC = 0.896, 95%CI: 0.850 to 0.942). The cross-validation of results showed that the accuracy of the evaluation database model was like that of the training. CONCLUSION In conclusion, a dozen symptoms that are easy to be clinically verified emerged as risk indicators for chronic kidney disease. This information can contribute to increasing awareness of the diagnosis, mainly in primary care settings. Therefore, healthcare professionals can select patients for more detailed investigation, which will reduce the chance of wasting time and improve early disease detection. WHAT IS KNOWN • Late diagnosis of chronic kidney disease in children is common, increasing morbidity. • Mass screening of the whole population is not cost-effective. WHAT IS NEW • With two machine-learning methods, this study revealed 12 symptoms to aid early CKD diagnosis. • These symptoms are easily obtainable and can be useful mainly in primary care settings.
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Affiliation(s)
- Paulo Cesar Koch Nogueira
- Pediatrics Department, UNIFESP - Escola Paulista de Medicina, Rua Guapiaçu 121 ap 91, 04024-020, Vila Clementino, Sao Paulo, Brazil.
- Pediatric Kidney Transplantation, Hospital Samaritano de Sao Paulo, Sao Paulo, Brazil.
| | | | | | - Tulio Konstantyner
- Pediatric Kidney Transplantation, Hospital Samaritano de Sao Paulo, Sao Paulo, Brazil
| | - Ricardo Sesso
- Medicine Department, UNIFESP - Escola Paulista de Medicina, Sao Paulo, Brazil
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Beaulieu-Jones BR, Shah S, Berrigan MT, Marwaha JS, Lai SL, Brat GA. Evaluating Capabilities of Large Language Models: Performance of GPT4 on Surgical Knowledge Assessments. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.16.23292743. [PMID: 37502981 PMCID: PMC10371188 DOI: 10.1101/2023.07.16.23292743] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Artificial intelligence (AI) has the potential to dramatically alter healthcare by enhancing how we diagnosis and treat disease. One promising AI model is ChatGPT, a large general-purpose language model trained by OpenAI. The chat interface has shown robust, human-level performance on several professional and academic benchmarks. We sought to probe its performance and stability over time on surgical case questions. Methods We evaluated the performance of ChatGPT-4 on two surgical knowledge assessments: the Surgical Council on Resident Education (SCORE) and a second commonly used knowledge assessment, referred to as Data-B. Questions were entered in two formats: open-ended and multiple choice. ChatGPT output were assessed for accuracy and insights by surgeon evaluators. We categorized reasons for model errors and the stability of performance on repeat encounters. Results A total of 167 SCORE and 112 Data-B questions were presented to the ChatGPT interface. ChatGPT correctly answered 71% and 68% of multiple-choice SCORE and Data-B questions, respectively. For both open-ended and multiple-choice questions, approximately two-thirds of ChatGPT responses contained non-obvious insights. Common reasons for inaccurate responses included: inaccurate information in a complex question (n=16, 36.4%); inaccurate information in fact-based question (n=11, 25.0%); and accurate information with circumstantial discrepancy (n=6, 13.6%). Upon repeat query, the answer selected by ChatGPT varied for 36.4% of inaccurate questions; the response accuracy changed for 6/16 questions. Conclusion Consistent with prior findings, we demonstrate robust near or above human-level performance of ChatGPT within the surgical domain. Unique to this study, we demonstrate a substantial inconsistency in ChatGPT responses with repeat query. This finding warrants future consideration and presents an opportunity to further train these models to provide safe and consistent responses. Without mental and/or conceptual models, it is unclear whether language models such as ChatGPT would be able to safely assist clinicians in providing care.
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Affiliation(s)
- Brendin R Beaulieu-Jones
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Sahaj Shah
- Geisinger Commonwealth School of Medicine, Scranton, PA
| | | | - Jayson S Marwaha
- Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Shuo-Lun Lai
- Division of Colorectal Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Gabriel A Brat
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
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Siddiqui AM, Thiele F, Stewart RN, Rangnick S, Weiss GJ, Chen BK, Silvernail JL, Strickland T, Nesbitt JJ, Lim K, Schwarzbauer JE, Schwartz J, Yaszemski MJ, Windebank AJ, Madigan NN. Open-Spaced Ridged Hydrogel Scaffolds Containing TiO 2-Self-Assembled Monolayer of Phosphonates Promote Regeneration and Recovery Following Spinal Cord Injury. Int J Mol Sci 2023; 24:10250. [PMID: 37373396 DOI: 10.3390/ijms241210250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/31/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The spinal cord has a poor ability to regenerate after an injury, which may be due to cell loss, cyst formation, inflammation, and scarring. A promising approach to treating a spinal cord injury (SCI) is the use of biomaterials. We have developed a novel hydrogel scaffold fabricated from oligo(poly(ethylene glycol) fumarate) (OPF) as a 0.08 mm thick sheet containing polymer ridges and a cell-attractive surface on the other side. When the cells are cultured on OPF via chemical patterning, the cells attach, align, and deposit ECM along the direction of the pattern. Animals implanted with the rolled scaffold sheets had greater hindlimb recovery compared to that of the multichannel scaffold control, which is likely due to the greater number of axons growing across it. The immune cell number (microglia or hemopoietic cells: 50-120 cells/mm2 in all conditions), scarring (5-10% in all conditions), and ECM deposits (Laminin or Fibronectin: approximately 10-20% in all conditions) were equal in all conditions. Overall, the results suggest that the scaffold sheets promote axon outgrowth that can be guided across the scaffold, thereby promoting hindlimb recovery. This study provides a hydrogel scaffold construct that can be used in vitro for cell characterization or in vivo for future neuroprosthetics, devices, or cell and ECM delivery.
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Affiliation(s)
- Ahad M Siddiqui
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Frederic Thiele
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Program in Human Medicine, Paracelsus Medical Private University, 5020 Salzburg, Austria
| | - Rachel N Stewart
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Regenerative Medicine Institute (REMEDI), National University of Ireland Galway, H91 TK33 Galway, Ireland
| | - Simone Rangnick
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Program in Human Medicine, Paracelsus Medical Private University, 5020 Salzburg, Austria
| | - Georgina J Weiss
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Program in Human Medicine, Paracelsus Medical Private University, 90419 Nuremberg, Germany
| | - Bingkun K Chen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Tammy Strickland
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Regenerative Medicine Institute (REMEDI), National University of Ireland Galway, H91 TK33 Galway, Ireland
| | | | - Kelly Lim
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Jean E Schwarzbauer
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Jeffrey Schwartz
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
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Galozzi P, Basso D, Plebani M, Padoan A. Artificial Intelligence and laboratory data in rheumatic diseases. Clin Chim Acta 2023; 546:117388. [PMID: 37187221 DOI: 10.1016/j.cca.2023.117388] [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: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
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Affiliation(s)
- Paola Galozzi
- Department of Medicine-DIMED, University of Padova, Padova, Italy.
| | - Daniela Basso
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
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Plass M, Kargl M, Kiehl TR, Regitnig P, Geißler C, Evans T, Zerbe N, Carvalho R, Holzinger A, Müller H. Explainability and causability in digital pathology. J Pathol Clin Res 2023. [PMID: 37045794 DOI: 10.1002/cjp2.322] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/17/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023]
Abstract
The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
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Affiliation(s)
- Markus Plass
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Michaela Kargl
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Tim-Rasmus Kiehl
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Peter Regitnig
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Christian Geißler
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Theodore Evans
- DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany
| | - Norman Zerbe
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Rita Carvalho
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany
| | - Andreas Holzinger
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Heimo Müller
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
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Ahmed U, Lin JCW, Srivastava G. Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases. SUSTAINABLE COMPUTING : INFORMATICS AND SYSTEMS 2023; 38:100868. [PMID: 37168459 PMCID: PMC10076073 DOI: 10.1016/j.suscom.2023.100868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 11/27/2022] [Accepted: 04/02/2023] [Indexed: 05/13/2023]
Abstract
Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention.
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Affiliation(s)
- Usman Ahmed
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063, Bergen, Norway
| | - Jerry Chun-Wei Lin
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, 5063, Bergen, Norway
| | - Gautam Srivastava
- Department of Mathematics & Computer Science, Brandon University, Brandon, Canada
- Research Centre of Interneural Computing, Taichung, Taiwan
- Department of Computer Science & Math, Lebanese American University, Beirut, Lebanon
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Wong CM, Kezlarian BE, Lin O. Current status of machine learning in thyroid cytopathology. J Pathol Inform 2023; 14:100309. [PMID: 37077698 PMCID: PMC10106504 DOI: 10.1016/j.jpi.2023.100309] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management.
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Affiliation(s)
| | | | - Oscar Lin
- Corresponding author at: Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
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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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Albahra S, Gorbett T, Robertson S, D'Aleo G, Kumar SVS, Ockunzzi S, Lallo D, Hu B, Rashidi HH. Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts. Semin Diagn Pathol 2023; 40:71-87. [PMID: 36870825 DOI: 10.1053/j.semdp.2023.02.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 02/17/2023]
Abstract
Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.
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Affiliation(s)
- Samer Albahra
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.
| | - Tom Gorbett
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Scott Robertson
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Giana D'Aleo
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Sushasree Vasudevan Suseel Kumar
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Samuel Ockunzzi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Daniel Lallo
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Bo Hu
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Hooman H Rashidi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States; PLMI's Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States.
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42
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Lee YS, Chow JC, Chien TW, Chou W. Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis. Medicine (Baltimore) 2023; 102:e33057. [PMID: 36827008 PMCID: PMC11309589 DOI: 10.1097/md.0000000000033057] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/25/2023] [Accepted: 02/01/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND The h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords. METHODS These 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores. RESULTS A coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%). CONCLUSION A total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.
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Affiliation(s)
- Yei-Soon Lee
- Department of Emergency Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | - Julie Chi Chow
- Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan
- Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chiali Chi-Mei Hospital, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
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Rashidi HH, Albahra S, Robertson S, Tran NK, Hu B. Common statistical concepts in the supervised Machine Learning arena. Front Oncol 2023; 13:1130229. [PMID: 36845729 PMCID: PMC9949554 DOI: 10.3389/fonc.2023.1130229] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 01/30/2023] [Indexed: 02/11/2023] Open
Abstract
One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.
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Affiliation(s)
- Hooman H. Rashidi
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States,PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States,*Correspondence: Hooman H. Rashidi, ; Bo Hu,
| | - Samer Albahra
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States,PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Scott Robertson
- Pathology and Laboratory Medicine Institute (PLMI), Cleveland Clinic, Cleveland, OH, United States,PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States
| | - Nam K. Tran
- Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, United States
| | - Bo Hu
- PLMI’s Center for Artificial Intelligence & Data Science, Cleveland Clinic, Cleveland, OH, United States,Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States,*Correspondence: Hooman H. Rashidi, ; Bo Hu,
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Ibidoja OJ, Shan FP, Mukhtar, Sulaiman J, Majahar Ali MK. Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data. JOURNAL OF THE NIGERIAN SOCIETY OF PHYSICAL SCIENCES 2023:1137. [DOI: 10.46481/jnsps.2023.1137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Abstract
A common problem in regression analysis using ordinary least squares (OLS) is the effect of outliers or contaminated data on the estimates of the parameters. A robust method that is not sensitive to outliers and can handle contaminated data is needed. In this study, the objective is to determine the significant parameters that determine the moisture content of the seaweed after drying and develop a hybrid model to reduce the outliers. The data were collected with sensors from the v-Groove Hybrid Solar Drier (v-GHSD) at Semporna, South-Eastern Coast of Sabah, Malaysia. After the second order interaction, we have 435 drying parameters, each parameter has 1914 observations. First, we used four machine learning algorithms, such as random forest, support vector machine, bagging and boosting to determine the significant parameters by selecting 15, 25, 35 and 45 parameters. Second, we developed the hybrid model using robust methods such as M. Bi-Square, M. Hampel and M. Huber. The results show that there is a significant improvement in the reduction of the number of outliers and better prediction using hybrid model for the contaminated seaweed big data. For the highest variable importance of 45 significant drying parameters of seaweed, the hybrid model bagging M Bi-square performs better because it has the lowest percentage of outliers of 4.08 %.
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Tran NK, LaValley C, Bagley B, Rodrigo J. Point of care blood glucose devices in the hospital setting. Crit Rev Clin Lab Sci 2023; 60:290-299. [PMID: 36734399 DOI: 10.1080/10408363.2023.2170316] [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: 02/04/2023]
Abstract
Dysglycemia is common among hospitalized patients. Accurate point-of-care (POC) glucose monitoring is necessary for the safe administration of insulin. Unfortunately, POC glucose meters are not all created equal. Interfering factors such as abnormal hematocrit, abnormal oxygen tension, and oxidizing/reducing substances can lead to inaccurate glucose measurements and result in inappropriate insulin dosing. The introduction of autocorrecting glucose meters has changed the POC testing landscape. Autocorrecting glucose meters provide more accurate measurements and have been associated with improved glycemic control in hospitalized patients. Continuous glucose monitoring has also created interest in using these platforms in at-risk inpatient populations. Future glucose monitoring technologies such as artificial intelligence/machine learning, wearable smart devices, and closed-loop insulin management systems are poised to transform glycemic management. The goal of this review is to provide an overview of glucose monitoring technology, summarize the clinical impact of glucose monitoring accuracy, and highlight emerging and future POC glucose monitoring technologies.
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Affiliation(s)
- Nam K Tran
- Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
| | - Clayton LaValley
- Patient Care Services, University of California Davis, Sacramento, CA, USA
| | - Berit Bagley
- Patient Care Services, University of California Davis, Sacramento, CA, USA
| | - John Rodrigo
- Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
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46
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Suba S, Muthulakshmi M. A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques. NETWORK (BRISTOL, ENGLAND) 2023; 34:26-64. [PMID: 36420865 DOI: 10.1080/0954898x.2022.2147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.
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Affiliation(s)
- Saravanan Suba
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
| | - M Muthulakshmi
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
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Karlafti E, Anagnostis A, Simou T, Kollatou AS, Paramythiotis D, Kaiafa G, Didaggelos T, Savvopoulos C, Fyntanidou V. Support Systems of Clinical Decisions in the Triage of the Emergency Department Using Artificial Intelligence: The Efficiency to Support Triage. Acta Med Litu 2023; 30:19-25. [PMID: 37575380 PMCID: PMC10417017 DOI: 10.15388/amed.2023.30.1.2] [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: 09/21/2022] [Revised: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 01/26/2023] Open
Abstract
Purpose In the Emergency Departments (ED) the current triage systems that are been implemented are based completely on medical education and the perception of each health professional who is in charge. On the other hand, cutting-edge technology, Artificial Intelligence (AI) can be incorporated into healthcare systems, supporting the healthcare professionals' decisions, and augmenting the performance of triage systems. The aim of the study is to investigate the efficiency of AI to support triage in ED. Patients–Methods The study included 332 patients from whom 23 different variables related to their condition were collected. From the processing of patient data for input variables, it emerged that the average age was 56.4 ± 21.1 years and 50.6% were male. The waiting time had an average of 59.7 ± 56.3 minutes while 3.9% ± 0.1% entered the Intensive Care Unit (ICU). In addition, qualitative variables related to the patient's history and admission clinics were used. As target variables were taken the days of stay in the hospital, which were on average 1.8 ± 5.9, and the Emergency Severity Index (ESI) for which the following distribution applies: ESI: 1, patients: 2; ESI: 2, patients: 18; ESI: 3, patients: 197; ESI: 4, patients: 73; ESI: 5, patients: 42. Results To create an automatic patient screening classifier, a neural network was developed, which was trained based on the data, so that it could predict each patient's ESI based on input variables.The classifier achieved an overall accuracy (F1 score) of 72.2% even though there was an imbalance in the classes. Conclusions The creation and implementation of an AI model for the automatic prediction of ESI, highlighted the possibility of systems capable of supporting healthcare professionals in the decision-making process. The accuracy of the classifier has not reached satisfactory levels of certainty, however, the performance of similar models can increase sharply with the collection of more data.
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Affiliation(s)
- Eleni Karlafti
- Emergency Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Athanasios Anagnostis
- Advanced Insights, Artificial Intelligence Solutions, Ipsilantou 10, Panorama, 55236 Thessaloniki, Greece
| | - Theodora Simou
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Angeliki Sevasti Kollatou
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Daniel Paramythiotis
- First Propaedeutic Surgery Department, AHEPA University General Hospital of Thessaloniki, 55636 Thessaloniki, Greece
| | - Georgia Kaiafa
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Triantafyllos Didaggelos
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Christos Savvopoulos
- First Propaedeutic Internal Medicine Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
| | - Varvara Fyntanidou
- Emergency Department, AHEPA University General Hospital of Thessaloniki, 55636, Thessaloniki, Greece
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The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update. Cancers (Basel) 2023; 15:cancers15030708. [PMID: 36765671 PMCID: PMC9913834 DOI: 10.3390/cancers15030708] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/27/2023] Open
Abstract
The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.
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Chandrashekar K, Setlur AS, Sabhapathi C A, Raiker SS, Singh S, Niranjan V. Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. Cancer Inform 2023; 22:11769351221147244. [PMID: 36714384 PMCID: PMC9880585 DOI: 10.1177/11769351221147244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 12/06/2022] [Indexed: 01/24/2023] Open
Abstract
Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.
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Affiliation(s)
| | | | | | | | | | - Vidya Niranjan
- Vidya Niranjan, Department of
Biotechnology, R V College of Engineering, Bengaluru, Karnataka 560059, India.
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Wu R, Luo J, Wan H, Zhang H, Yuan Y, Hu H, Feng J, Wen J, Wang Y, Li J, Liang Q, Gan F, Zhang G. Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database. PLoS One 2023; 18:e0280340. [PMID: 36701415 PMCID: PMC9879508 DOI: 10.1371/journal.pone.0280340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 12/26/2022] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. OBJECTIVE The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. METHODS This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. RESULTS Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). CONCLUSIONS The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.
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Affiliation(s)
- Ruiyang Wu
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jing Luo
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Hangyu Wan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Haiyan Zhang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Yewei Yuan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Huihua Hu
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jinyan Feng
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Jing Wen
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Yan Wang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Junyan Li
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Qi Liang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Fengjiao Gan
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
| | - Gang Zhang
- Department of Breast and Thyroid Surgery, Sichuan Provincial Hospital for Women and Children (Affiliated Women and Children’s Hospital of Chengdu Medical College), Chengdu, China
- * E-mail:
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