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Franco D’Souza R, Mathew M, Mishra V, Surapaneni KM. Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Med Educ Online 2024; 29:2330250. [PMID: 38566608 PMCID: PMC10993743 DOI: 10.1080/10872981.2024.2330250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024]
Abstract
Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.
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Affiliation(s)
- Russell Franco D’Souza
- Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia
- Department of Organisational Psychological Medicine, International Institute of Organisational Psychological Medicine, Melbourne, Australia
| | - Mary Mathew
- Department of Pathology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Vedprakash Mishra
- School of Hogher Education and Research, Datta Meghe Institute of Higher Education and Research (Deemed to be University), Nagpur, India
| | - Krishna Mohan Surapaneni
- Department of Biochemistry, Panimalar Medical College Hospital & Research Institute, Chennai, India
- Department of Medical Education, Panimalar Medical College Hospital & Research Institute, Chennai, India
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Park SH, Pinto-Powell R, Thesen T, Lindqwister A, Levy J, Chacko R, Gonzalez D, Bridges C, Schwendt A, Byrum T, Fong J, Shasavari S, Hassanpour S. Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. Med Educ Online 2024; 29:2315684. [PMID: 38351737 PMCID: PMC10868429 DOI: 10.1080/10872981.2024.2315684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.
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Affiliation(s)
- Soo Hwan Park
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Thomas Thesen
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Joshua Levy
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Rachael Chacko
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | | | - Connor Bridges
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Adam Schwendt
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Travis Byrum
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Justin Fong
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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Chang CC, Liu TC, Lu CJ, Chiu HC, Lin WN. Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis. Comput Struct Biotechnol J 2024; 23:1572-1583. [PMID: 38650589 PMCID: PMC11035017 DOI: 10.1016/j.csbj.2024.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/25/2024] Open
Abstract
Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.
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Affiliation(s)
- Che-Cheng Chang
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Wei-Ning Lin
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
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Hang JF, Ou YC, Yang WL, Tsao TY, Yeh CH, Li CB, Hsu EY, Hung PY, Hwang YT, Liu TJ, Tung MC. Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology. J Pathol Inform 2024; 15:100346. [PMID: 38125926 PMCID: PMC10730371 DOI: 10.1016/j.jpi.2023.100346] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/22/2023] [Accepted: 11/01/2023] [Indexed: 12/23/2023] Open
Abstract
Background Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes. Methods Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size. Results The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes. Discussion Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 μm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.
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Affiliation(s)
- Jen-Fan Hang
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine and Institution of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Tang-Yi Tsao
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
| | | | - Chi-Bin Li
- AIxMed, Inc., Santa Clara, CA, United States
| | - En-Yu Hsu
- AIxMed, Inc., Santa Clara, CA, United States
| | - Po-Yen Hung
- AIxMed, Inc., Santa Clara, CA, United States
| | - Yi-Ting Hwang
- Department of Statistics, National Taipei University, Taipei, Taiwan
| | | | - Min-Che Tung
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan
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Kim BR, Kim MJ, Koo J, Choi HJ, Paik KH, Kwon SH, Choi HR, Huh CH, Shin JW, Park DS, Na JI. Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants. J DERMATOL TREAT 2024; 35:2337908. [PMID: 38616301 DOI: 10.1080/09546634.2024.2337908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/01/2023] [Indexed: 04/16/2024]
Abstract
Background: Scalp-related symptoms such as dandruff and itching are common with diverse underlying etiologies. We previously proposed a novel classification and scoring system for scalp conditions, called the scalp photographic index (SPI); it grades five scalp features using trichoscopic images with good reliability. However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. Using the SPI, the scalp conditions were evaluated at baseline and at weeks 4, 8, and 12 of treatment.Results: The accuracies of the SPI-AI for dryness, oiliness, erythema, folliculitis, and dandruff were 91.3%, 90.5%, 89.6%, 87.3%, and 95.2%, respectively. Overall, 100 individuals completed the 4-week study; 43 of these participated in an extension study until week 12. The total SPI score decreased from 32.70 ± 7.40 at baseline to 15.97 ± 4.68 at week 4 (p < 0.001). The efficacy was maintained throughout 12 weeks.Conclusions: SPI-AI accurately assessed the scalp condition. AI-based prescription of tailored scalp cosmetics could significantly improve scalp health.
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Affiliation(s)
- Bo Ri Kim
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Min Jae Kim
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Jieun Koo
- Aram Huvis Co., Ltd, Seongnam, Korea
| | - Hwa-Jung Choi
- Department of Beauty Art, Youngsan University, Busan, South Korea
| | - Kyung Ho Paik
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Soon Hyo Kwon
- Department of Dermatology, Kyung Hee University Hospital at Gangdong, Seoul, Korea
| | - Hye-Ryung Choi
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Chang Hun Huh
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Won Shin
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
| | | | - Jung-Im Na
- Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Dermatology, Seoul National University College of Medicine, Seoul, Korea
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Li C, Luo Y, Xie Y, Zhang Z, Liu Y, Zou L, Xiao F. Structural and functional prediction, evaluation, and validation in the post-sequencing era. Comput Struct Biotechnol J 2024; 23:446-451. [PMID: 38223342 PMCID: PMC10787220 DOI: 10.1016/j.csbj.2023.12.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/16/2024] Open
Abstract
The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.
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Affiliation(s)
- Chang Li
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yixuan Luo
- Beijing Normal University, Beijing, China
| | - Yibo Xie
- Information Center, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Zaifeng Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ye Liu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Lihui Zou
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Fei Xiao
- Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Normal University, Beijing, China
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Garcia Valencia OA, Thongprayoon C, Miao J, Bruminhent J, Craici IM, Cheungpasitporn W. Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era. Ren Fail 2024; 46:2337291. [PMID: 38584142 PMCID: PMC11000603 DOI: 10.1080/0886022x.2024.2337291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.
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Affiliation(s)
- Oscar A Garcia Valencia
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jing Miao
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jackrapong Bruminhent
- Department of Medicine, Division of Infectious Diseases, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Ramathibodi Excellence Center for Organ Transplantation, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Iasmina M Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
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Rajendran R, Beck RC, Waskasi MM, Kelly BD, Bauer DR. Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing. J Pathol Inform 2024; 15:100352. [PMID: 38186745 PMCID: PMC10770522 DOI: 10.1016/j.jpi.2023.100352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/30/2023] [Accepted: 11/16/2023] [Indexed: 01/09/2024] Open
Abstract
As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.
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Affiliation(s)
- Rahul Rajendran
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Rachel C. Beck
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Morteza M. Waskasi
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Brian D. Kelly
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
| | - Daniel R. Bauer
- Roche Diagnostics Solutions, (Ventana Medical Systems, Inc.), Tucson, AZ, USA
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Mehrabi Nasab E, Sadeghian S, Vasheghani Farahani A, Yamini Sharif A, Masoud Kabir F, Bavanpour Karvane H, Zahedi A, Bozorgi A. Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model. Regen Ther 2024; 27:32-38. [PMID: 38496010 PMCID: PMC10940794 DOI: 10.1016/j.reth.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 03/19/2024] Open
Abstract
Ventricular arrhythmias increase cardiovascular morbidity and mortality. Recurrent PVCs and IVT are generally considered benign in the absence of structural heart abnormalities. Artificial intelligence is a rapidly growing field. In recent years, medical professionals have shown great interest in the potential use of ML, an integral part of AI, in various disciplines, including diagnostic applications, decision-making, prognostic stratification, and solving complex pathophysiological aspects of diseases from these data at extraordinary complexity, scale, and acquisition rate. The aim of this study was to design an ML model to predict the probability of PVC and IVT recurrence after RF ablation. Data of patients were collected and manipulated using traditional analysis and various artificial intelligence models, namely MLP, Gradient Boosting Machines, Random Forest, and Logistic Regression. Hypertension, male sex, and the use of non-irrigate catheters were associated with less freedom from arrhythmia. All these results were obtained through traditional analytic methods, and according to AI, none of the variables had a clear effect on the recurrence of arrhythmia. Each AI model presents unique strengths and weaknesses, and further optimization and fine-tuning of these models are necessary to increase their clinical utility. By expanding the dataset, improved predictions can be fostered to ultimately increase the clinical utility of AI in predicting PVC erosion outcomes.
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Affiliation(s)
- Entezar Mehrabi Nasab
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Cardiology, School of Medicine, Valiasr Hospital, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Saeed Sadeghian
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Vasheghani Farahani
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ahmad Yamini Sharif
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoud Kabir
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ahora Zahedi
- Department of Artificial Intelligence in Medical Sciences, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Ali Bozorgi
- Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
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Lella KK, Jagadeesh MS, Alphonse PJA. Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN. Health Inf Sci Syst 2024; 12:22. [PMID: 38469455 PMCID: PMC10924857 DOI: 10.1007/s13755-024-00283-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (1 × 12 , 1 × 24 , 1 × 36 , 1 × 48 , and 1 × 60 ) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model's effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.
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Affiliation(s)
- Kranthi Kumar Lella
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - M. S. Jagadeesh
- School of Computer Science and Engineering, VIT-AP University, Vijayawada, Guntur, Andhra Pradesh 522237 India
| | - P. J. A. Alphonse
- Department of Computer Applications, NIT Tiruchirappalli, Tiruchirappalli, Guntur, Tamil Nadu 620015 India
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Lee S, Arffman RK, Komsi EK, Lindgren O, Kemppainen J, Kask K, Saare M, Salumets A, Piltonen TT. Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium. J Pathol Inform 2024; 15:100364. [PMID: 38445292 PMCID: PMC10914580 DOI: 10.1016/j.jpi.2024.100364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 03/07/2024] Open
Abstract
Background The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency. Methods We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition. Results Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists' assessment, with F1 scores exceeding 82% for the epithelium and >96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women's samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR < 0.001; PCOS SE, r2 = 0.52, FDR < 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS PE, 15.56%, p = 1.00). We did not observe significant differences in the epithelial-to-stroma ratio in the hormone-induced endometrium in RIF patients with different receptivity statuses. Conclusion The AI model rapidly and accurately identifies endometrial histology features by calculating areas occupied by epithelial and stromal cells. The AI model demonstrates changes in epithelial cellular proportions according to the menstrual cycle phase and reveals no changes in epithelial cellular proportions based on PCOS and RIF conditions. In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.
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Affiliation(s)
- Seungbaek Lee
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
| | - Riikka K. Arffman
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Elina K. Komsi
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
| | - Outi Lindgren
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Janette Kemppainen
- Department of Pathology, Oulu University Hospital, Cancer and Translational Medicine Research Unit, University of Oulu, Oulu 90220, Finland
| | - Keiu Kask
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Merli Saare
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
| | - Andres Salumets
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu 50406, Estonia
- Competence Centre on Health Technologies, Tartu 51014, Estonia
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm 14152, Sweden
| | - Terhi T. Piltonen
- Department of Obstetrics and Gynaecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu 90220, Finland
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Cheng J, Schmidt C, Wilson A, Wang Z, Hao W, Pantanowitz J, Morris C, Tashjian R, Pantanowitz L. Artificial intelligence for human gunshot wound classification. J Pathol Inform 2024; 15:100361. [PMID: 38234590 PMCID: PMC10792621 DOI: 10.1016/j.jpi.2023.100361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/18/2023] [Accepted: 12/26/2023] [Indexed: 01/19/2024] Open
Abstract
Certain features are helpful in the identification of gunshot entrance and exit wounds, such as the presence of muzzle imprints, peripheral tears, stippling, bone beveling, and wound border irregularity. Some cases are less straightforward and wounds can thus pose challenges to an emergency room doctor or forensic pathologist. In recent years, deep learning has shown promise in various automated medical image classification tasks. This study explores the feasibility of using a deep learning model to classify entry and exit gunshot wounds in digital color images. A collection of 2418 images of entrance and exit gunshot wounds were procured. Of these, 2028 entrance and 1314 exit wounds were cropped, focusing on the area around each gunshot wound. A ConvNext Tiny deep learning model was trained using the Fastai deep learning library, with a train/validation split ratio of 70/30, until a maximum validation accuracy of 92.6% was achieved. An additional 415 entrance and 293 exit wound images were collected for the test (holdout) set. The model achieved an accuracy of 87.99%, precision of 83.99%, recall of 87.71%, and F1-score 85.81% on the holdout set. Correctly classified were 88.19% of entrance wounds and 87.71% of exit wounds. The results are comparable to what a forensic pathologist can achieve without other morphologic cues. This study represents one of the first applications of artificial intelligence to the field of forensic pathology. This work demonstrates that deep learning models can discern entrance and exit gunshot wounds in digital images with high accuracy.
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Affiliation(s)
- Jerome Cheng
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Carl Schmidt
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Allecia Wilson
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Zixi Wang
- Biostatistics Department, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wei Hao
- Biostatistics Department, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Joshua Pantanowitz
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Catherine Morris
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Randy Tashjian
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
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Sarumi OA, Hahn M, Heider D. NeuralBeds: Neural embeddings for efficient DNA data compression and optimized similarity search. Comput Struct Biotechnol J 2024; 23:732-741. [PMID: 38298179 PMCID: PMC10828564 DOI: 10.1016/j.csbj.2023.12.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/28/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024] Open
Abstract
The availability of high throughput sequencing tools coupled with the declining costs in the production of DNA sequences has led to the generation of enormous amounts of omics data curated in several databases such as NCBI and EMBL. Identification of similar DNA sequences from these databases is one of the fundamental tasks in bioinformatics. It is essential for discovering homologous sequences in organisms, phylogenetic studies of evolutionary relationships among several biological entities, or detection of pathogens. Improving DNA similarity search is of outmost importance because of the increased complexity of the evergrowing repositories of sequences. Therefore, instead of using the conventional approach of comparing raw sequences, e.g., in fasta format, a numerical representation of the sequences can be used to calculate their similarities and optimize the search process. In this study, we analyzed different approaches for numerical embeddings, including Chaos Game Representation, hashing, and neural networks, and compared them with classical approaches such as principal component analysis. It turned out that neural networks generate embeddings that are able to capture the similarity between DNA sequences as a distance measure and outperform the other approaches on DNA similarity search, significantly.
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Affiliation(s)
- Oluwafemi A. Sarumi
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
- Institute of Computer Science, Heinrich-Heine-University Duesseldorf, Graf-Adolf-Str. 63, Duesseldorf, D-40215, Germany
| | - Maximilian Hahn
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, D-35043, Germany
- Institute of Computer Science, Heinrich-Heine-University Duesseldorf, Graf-Adolf-Str. 63, Duesseldorf, D-40215, Germany
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Makhlouf Y, Singh VK, Craig S, McArdle A, French D, Loughrey MB, Oliver N, Acevedo JB, O’Reilly P, James JA, Maxwell P, Salto-Tellez M. True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images. Comput Struct Biotechnol J 2024; 23:174-185. [PMID: 38146436 PMCID: PMC10749253 DOI: 10.1016/j.csbj.2023.11.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/27/2023] Open
Abstract
The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.
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Affiliation(s)
- Yasmine Makhlouf
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Vivek Kumar Singh
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Stephanie Craig
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Aoife McArdle
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Dominique French
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Maurice B. Loughrey
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Cellular Pathology, Belfast Health and Social Care Trust, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, UK
| | - Nicola Oliver
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Juvenal Baena Acevedo
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | | | - Jacqueline A. James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK
| | - Perry Maxwell
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK
- Sonrai Analytics, Belfast BT9 7AE, UK
- Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK
- Integrated Pathology Unit, Institute of Cancer Research and Royal Marsden Hospital, London SW7 3RP, UK
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Wang YL, Gao S, Xiao Q, Li C, Grzegorzek M, Zhang YY, Li XH, Kang Y, Liu FH, Huang DH, Gong TT, Wu QJ. Role of artificial intelligence in digital pathology for gynecological cancers. Comput Struct Biotechnol J 2024; 24:205-212. [PMID: 38510535 PMCID: PMC10951449 DOI: 10.1016/j.csbj.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024] Open
Abstract
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
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Affiliation(s)
- Ya-Li Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Information Center, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Ying-Ying Zhang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiao-Han Li
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dong-Hui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China
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Di J, Hickey C, Bumgardner C, Yousif M, Zapata M, Bocklage T, Balzer B, Bui MM, Gardner JM, Pantanowitz L, Qasem SA. Utility of artificial intelligence in a binary classification of soft tissue tumors. J Pathol Inform 2024; 15:100368. [PMID: 38496781 PMCID: PMC10940995 DOI: 10.1016/j.jpi.2024.100368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/25/2024] [Accepted: 02/09/2024] [Indexed: 03/19/2024] Open
Abstract
Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.
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Affiliation(s)
- Jing Di
- University of Kentucky College of Medicine, Lexington, KY, United States
| | - Caylin Hickey
- University of Kentucky College of Medicine, Lexington, KY, United States
| | - Cody Bumgardner
- University of Kentucky College of Medicine, Lexington, KY, United States
| | | | | | - Therese Bocklage
- University of Kentucky College of Medicine, Lexington, KY, United States
| | - Bonnie Balzer
- Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Marilyn M. Bui
- Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | | | - Liron Pantanowitz
- University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Shadi A. Qasem
- University of Kentucky College of Medicine, Lexington, KY, United States
- Baptist Health Jacksonville, Jacksonville, FL, United States
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Schmauch B, Elsoukkary SS, Moro A, Raj R, Wehrle CJ, Sasaki K, Calderaro J, Sin-Chan P, Aucejo F, Roberts DE. Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery. J Pathol Inform 2024; 15:100360. [PMID: 38292073 PMCID: PMC10825615 DOI: 10.1016/j.jpi.2023.100360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/10/2023] [Accepted: 12/23/2023] [Indexed: 02/01/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.
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Affiliation(s)
| | - Sarah S. Elsoukkary
- Owkin Lab, Owkin, Inc., New York, NY, USA
- Department of Pathology, Cleveland Clinic, Cleveland, OH, USA
| | - Amika Moro
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Roma Raj
- Department of Surgery, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kazunari Sasaki
- Department of Surgery, Stanford University, Palo Alto, CA, USA
| | - Julien Calderaro
- Department of Pathology, Henri Mondor University Hospital, Créteil, France
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Budginaite E, Magee DR, Kloft M, Woodruff HC, Grabsch HI. Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review. J Pathol Inform 2024; 15:100367. [PMID: 38455864 PMCID: PMC10918266 DOI: 10.1016/j.jpi.2024.100367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
Background Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured. Objective To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research. Methods A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles. Results A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible. Conclusions Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.
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Affiliation(s)
- Elzbieta Budginaite
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | | | - Maximilian Kloft
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Internal Medicine, Justus-Liebig-University, Giessen, Germany
| | - Henry C. Woodruff
- Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Heike I. Grabsch
- Department of Pathology, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK
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Stenman S, Bétrisey S, Vainio P, Huvila J, Lundin M, Linder N, Schmitt A, Perren A, Dettmer MS, Haglund C, Arola J, Lundin J. External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study. J Pathol Inform 2024; 15:100366. [PMID: 38425542 PMCID: PMC10901856 DOI: 10.1016/j.jpi.2024.100366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/03/2024] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.
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Affiliation(s)
- Sebastian Stenman
- Institute for Molecular Medicine Finland – FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland
- HUSLAB, Department of Pathology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Haartmaninkatu 3C, 000290 HUS Helsinki, Finland
- Department of Surgery, Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Sylvain Bétrisey
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland
| | - Paula Vainio
- Department of Pathology, University of Turku, Turku University Hospital, Kiinamyllykatu 10, 20520 Turku, Finland
| | - Jutta Huvila
- Department of Pathology, University of Turku, Turku University Hospital, Kiinamyllykatu 10, 20520 Turku, Finland
| | - Mikael Lundin
- Institute for Molecular Medicine Finland – FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland
| | - Nina Linder
- Institute for Molecular Medicine Finland – FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland
- Institute of Pathology, Klinikum Stuttgart, Kriegsbergstraße 60, 70174 Stuttgart, Germany
| | - Anja Schmitt
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland
| | - Aurel Perren
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland
| | - Matthias S. Dettmer
- Institute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland
- The Global Health & Migration Department of Women’s and Children’s Health, Uppsala University, 75185 Uppsala, Sweden
| | - Caj Haglund
- Department of Surgery, Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
- Research Programs Unit, Translational Cancer Medicine, University of Helsinki, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Johanna Arola
- HUSLAB, Department of Pathology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Haartmaninkatu 3C, 000290 HUS Helsinki, Finland
| | - Johan Lundin
- Institute for Molecular Medicine Finland – FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland
- Department of Global Public Health, Karolinska Institutet, Norrbackagatan 4, 17176 Stockholm, Sweden
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
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Zheng H, Xu L, Xie H, Xie J, Ma Y, Hu Y, Wu L, Chen J, Wang M, Yi Y, Huang Y, Wang D. RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature. Comput Struct Biotechnol J 2024; 23:1469-1476. [PMID: 38623560 PMCID: PMC11016866 DOI: 10.1016/j.csbj.2024.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024] Open
Abstract
RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.
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Affiliation(s)
- Hailong Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Linfu Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Hailong Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, 361102 Xiamen, China
| | - Yapeng Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Le Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jia Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Meiyi Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Ying Yi
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, 510515, Guangzhou, China
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Dong X, Chen G, Zhu Y, Ma B, Ban X, Wu N, Ming Y. Artificial intelligence in skeletal metastasis imaging. Comput Struct Biotechnol J 2024; 23:157-164. [PMID: 38144945 PMCID: PMC10749216 DOI: 10.1016/j.csbj.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 12/26/2023] Open
Abstract
In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.
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Affiliation(s)
- Xiying Dong
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100021 Beijing, China
| | - Guilin Chen
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Graduate School of Peking Union Medical College, Beijing 100730, China
| | - Yuanpeng Zhu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Graduate School of Peking Union Medical College, Beijing 100730, China
| | - Boyuan Ma
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Xiaojuan Ban
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Nan Wu
- Department of Orthopedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Big Data for Spinal Deformities, Chinese Academy of Medical Sciences, Beijing 100730, China
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Beijing 100730, China
| | - Yue Ming
- Department of Nuclear Medicine (PET-CT Center), National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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Kurniawan MH, Handiyani H, Nuraini T, Hariyati RTS, Sutrisno S. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Ann Med 2024; 56:2302980. [PMID: 38466897 PMCID: PMC10930147 DOI: 10.1080/07853890.2024.2302980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/31/2023] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. METHOD A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. RESULTS Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. CONCLUSIONS The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.
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Affiliation(s)
- Moh Heri Kurniawan
- Doctoral Student, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
| | - Hanny Handiyani
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | - Tuti Nuraini
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | | | - Sutrisno Sutrisno
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
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Edvinsson C, Björnsson O, Erlandsson L, Hansson SR. Predicting intensive care need in women with preeclampsia using machine learning - a pilot study. Hypertens Pregnancy 2024; 43:2312165. [PMID: 38385188 DOI: 10.1080/10641955.2024.2312165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/02/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics. METHODS We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models. RESULTS The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. Our model was internally validated on a test-set where the accuracy was lower, 0.82, with an AUC of 0.85. CONCLUSION The clinical routine blood parameters ASAT and uric acid as well as BMI, were the parameters most indicative of severe disease. Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..[Figure: see text].
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Affiliation(s)
- Camilla Edvinsson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Division of Anaesthesia and Intensive Care, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Ola Björnsson
- Division of Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, Sweden
- Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden
| | - Lena Erlandsson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Stefan R Hansson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Obstetrics and Gynecology, Skåne University Hospital, Lund/Malmö, Sweden
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Martino Cinnera A, Morone G, Iosa M, Bonomi S, Calabrò RS, Tonin P, Cerasa A, Ricci A, Ciancarelli I. Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study. J Orthop 2024; 55:38-43. [PMID: 38638115 PMCID: PMC11021912 DOI: 10.1016/j.jor.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/20/2024] Open
Abstract
Background and aim Lumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network. Methods Adult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression. Results 108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome. Conclusions ANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.
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Affiliation(s)
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
- San Raffaele Institute of Sulmona, Sulmona, Italy
| | - Marco Iosa
- IRCCS Santa Lucia Foundation Hospital, Rome, Italy
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | | | | | | | - Antonio Cerasa
- Sant'Anna Institute, Crotone, Italy
- Institute for Biomedical Research and Innovation, National Research Council of Italy (IRIB-CNR), Messina, Italy
- Pharmacotechnology Documention and Transfer Unit, Preclinical and Traslation Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Arcavacata, Italy
| | - Alessandro Ricci
- Department of Neurosurgery, San Salvatore Hospital, ASL Avezzano-Sulmona-L’Aquila, L'Aquila, Italy
| | - Irene Ciancarelli
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
- Territorial Rehabilitation, ASL Avezzano-Sulmona-L’Aquila, L'Aquila, Italy
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Nath PC, Mishra AK, Sharma R, Bhunia B, Mishra B, Tiwari A, Nayak PK, Sharma M, Bhuyan T, Kaushal S, Mohanta YK, Sridhar K. Recent advances in artificial intelligence towards the sustainable future of agri-food industry. Food Chem 2024; 447:138945. [PMID: 38461725 DOI: 10.1016/j.foodchem.2024.138945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/26/2024] [Accepted: 03/02/2024] [Indexed: 03/12/2024]
Abstract
Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a significant uptick in recent years. Therefore, comprehensive understanding of these techniques is needed to broaden its application in agri-food supply chain. In this review, we explored cutting-edge artificial intelligence methodologies with a focus on machine learning, neural networks, and deep learning. The application of artificial intelligence in agri-food industry and their quality assurance throughout the production process is thoroughly discussed with an emphasis on the current scientific knowledge and future perspective. Artificial intelligence has played a significant role in transforming agri-food systems by enhancing efficiency, sustainability, and productivity. Many food industries are implementing the artificial intelligence in modelling, prediction, control tool, sensory evaluation, quality control, and tackling complicated challenges in food processing. Similarly, artificial intelligence applied in agriculture to improve the entire farming process, such as crop yield optimization, use of herbicides, weeds identification, and harvesting of fruits. In summary, the integration of artificial intelligence in agri-food systems offers the potential to address key challenges in agriculture, enhance sustainability, and contribute to global food security.
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Affiliation(s)
- Pinku Chandra Nath
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Awdhesh Kumar Mishra
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea
| | - Ramesh Sharma
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India; Sri Shakthi Institute of Engineering and Technology, Chinniyampalayam, 641062 Coimbatore, India
| | - Biswanath Bhunia
- Bioproducts Processing Research Laboratory (BPRL), Department of Bio Engineering, National Institute of Technology, Agartala 799046, India
| | - Bishwambhar Mishra
- Department of Biotechnology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India
| | - Ajita Tiwari
- Department of Agricultural Engineering, Assam University, Silchar 788011, India
| | - Prakash Kumar Nayak
- Department of Food Engineering and Technology, Central Institute of Technology Kokrajhar, Kokrajhar 783370, India
| | - Minaxi Sharma
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Tamanna Bhuyan
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India
| | - Sushant Kaushal
- Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
| | - Yugal Kishore Mohanta
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India; Centre for Herbal Pharmacology and Environmental Sustainability, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam 603103, India.
| | - Kandi Sridhar
- Department of Food Technology, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, India.
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Bernardini LG, Rosinger C, Bodner G, Keiblinger KM, Izquierdo-Verdiguier E, Spiegel H, Retzlaff CO, Holzinger A. Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction? N Biotechnol 2024; 81:20-31. [PMID: 38462171 DOI: 10.1016/j.nbt.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/24/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.
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Affiliation(s)
| | - Christoph Rosinger
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria; Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria.
| | - Gernot Bodner
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| | - Katharina M Keiblinger
- Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Emma Izquierdo-Verdiguier
- Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Heide Spiegel
- Austrian Agency for Health and Food Safety (AGES), Institute for Soil Health and Plant Nutrition, Spargelfeldstraße 191, 1226 Vienna, Austria
| | - Carl O Retzlaff
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
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Vinothkanna A, Dar OI, Liu Z, Jia AQ. Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents. Food Chem 2024; 446:138893. [PMID: 38432137 DOI: 10.1016/j.foodchem.2024.138893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/15/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.
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Affiliation(s)
- Annadurai Vinothkanna
- School of Life and Health Sciences, Hainan University, Haikou 570228, China; Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
| | - Owias Iqbal Dar
- School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China
| | - Zhu Liu
- School of Life and Health Sciences, Hainan University, Haikou 570228, China.
| | - Ai-Qun Jia
- Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China.
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Awuah WA, Adebusoye FT, Wellington J, David L, Salam A, Weng Yee AL, Lansiaux E, Yarlagadda R, Garg T, Abdul-Rahman T, Kalmanovich J, Miteu GD, Kundu M, Mykolaivna NI. Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery. World Neurosurg X 2024; 23:100301. [PMID: 38577317 PMCID: PMC10992893 DOI: 10.1016/j.wnsx.2024.100301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/23/2023] [Accepted: 02/21/2024] [Indexed: 04/06/2024] Open
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Affiliation(s)
| | | | - Jack Wellington
- Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom
| | - Lian David
- Norwich Medical School, University of East Anglia, United Kingdom
| | - Abdus Salam
- Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan
| | | | | | - Rohan Yarlagadda
- Rowan University School of Osteopathic Medicine, Stratford, NJ, USA
| | - Tulika Garg
- Government Medical College and Hospital Chandigarh, India
| | | | | | | | - Mrinmoy Kundu
- Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India
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Anish RJ, Nair A. Osteoporosis management-current and future perspectives - A systemic review. J Orthop 2024; 53:101-113. [PMID: 38495575 PMCID: PMC10940894 DOI: 10.1016/j.jor.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/23/2024] [Accepted: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction Osteoporosis is a geriatric metabolic ailment distinguished by low bone mineral density (BMD) and strength with enhanced micro-architectural retrogression of the extracellular matrix, further increasing bone fragility risk. Osteoporotic fractures and associated complications become common in women and men after 55 and 65 years, respectively. The loss in BMD markedly enhances the risk of fracture, non-skeletal injury, and subsequent pain, adversely affecting the quality of life. Methods Data summarised in this review were sourced and summarised, including contributions from 2008 to 2023, online from scientific search engines, based on scientific inclusion and exclusion criteria. Results Biochemical serum markers such as BALP, collagen, osteocalcin, and cathepsin-K levels can reveal the osteoporotic status. DEXA scan techniques evaluate the whole body's BMD and bone mineral content (BMC), crucial in osteoporosis management. Anabolic and anti-osteoporotic agents are commonly used to enhance bone formation, minimize bone resorption, and regulate remodelling. The challenges and side effects of drug therapy can be overcome by combining the various drug moieties. Conclusion The current review discusses the management protocol for osteoporosis, ranging from lifestyle modification, including physical exercise, pharmaceutical approaches, drug delivery applications, and advanced therapeutic possibilities of AI and machine learning techniques to reduce osteoporosis complications and fracture risk.
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Affiliation(s)
- Rajamohanan Jalaja Anish
- Department of Biochemistry, University of Kerala, Kariyavattom Campus, Trivandrum, 695581, India
| | - Aswathy Nair
- Department of Biochemistry, University of Kerala, Kariyavattom Campus, Trivandrum, 695581, India
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Xiong F, Su Z, Tang Y, Dai T, Wen D. Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence. Environ Sci Ecotechnol 2024; 20:100370. [PMID: 38292137 PMCID: PMC10826124 DOI: 10.1016/j.ese.2023.100370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 02/01/2024]
Abstract
Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs' performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.
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Affiliation(s)
- Fuzhong Xiong
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Zhiguo Su
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yushi Tang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
| | - Tianjiao Dai
- School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China
| | - Donghui Wen
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
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Mai J, Lachinov D, Reiter GS, Riedl S, Grechenig C, Bogunovic H, Schmidt-Erfurth U. Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT. Ophthalmol Sci 2024; 4:100466. [PMID: 38591046 PMCID: PMC11000109 DOI: 10.1016/j.xops.2024.100466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/08/2023] [Accepted: 01/09/2024] [Indexed: 04/10/2024]
Abstract
Objective To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care. Design Clinical evaluation of a deep learning-based algorithm. Subjects One hundred eighty-four eyes of 100 consecutively enrolled patients. Methods OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed. Main Outcome Measures Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth. Results The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively. Conclusions The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Julia Mai
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Dmitrii Lachinov
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Gregor S. Reiter
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Sophie Riedl
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Christoph Grechenig
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Hrvoje Bogunovic
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Ursula Schmidt-Erfurth
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
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Tailor PD, Dalvin LA, Chen JJ, Iezzi R, Olsen TW, Scruggs BA, Barkmeier AJ, Bakri SJ, Ryan EH, Tang PH, Parke DW, Belin PJ, Sridhar J, Xu D, Kuriyan AE, Yonekawa Y, Starr MR. A Comparative Study of Responses to Retina Questions from Either Experts, Expert-Edited Large Language Models, or Expert-Edited Large Language Models Alone. Ophthalmol Sci 2024; 4:100485. [PMID: 38660460 PMCID: PMC11041826 DOI: 10.1016/j.xops.2024.100485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/03/2024] [Accepted: 02/01/2024] [Indexed: 04/26/2024]
Abstract
Objective To assess the quality, empathy, and safety of expert edited large language model (LLM), human expert created, and LLM responses to common retina patient questions. Design Randomized, masked multicenter study. Participants Twenty-one common retina patient questions were randomly assigned among 13 retina specialists. Methods Each expert created a response (Expert) and then edited a LLM (ChatGPT-4)-generated response to that question (Expert + artificial intelligence [AI]), timing themselves for both tasks. Five LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, and Bard) also generated responses to each question. The original question along with anonymized and randomized Expert + AI, Expert, and LLM responses were evaluated by the other experts who did not write an expert response to the question. Evaluators judged quality and empathy (very poor, poor, acceptable, good, or very good) along with safety metrics (incorrect information, likelihood to cause harm, extent of harm, and missing content). Main Outcome Mean quality and empathy score, proportion of responses with incorrect information, likelihood to cause harm, extent of harm, and missing content for each response type. Results There were 4008 total grades collected (2608 for quality and empathy; 1400 for safety metrics), with significant differences in both quality and empathy (P < 0.001, P < 0.001) between LLM, Expert and Expert + AI groups. For quality, Expert + AI (3.86 ± 0.85) performed the best overall while GPT-3.5 (3.75 ± 0.79) was the top performing LLM. For empathy, GPT-3.5 (3.75 ± 0.69) had the highest mean score followed by Expert + AI (3.73 ± 0.63). By mean score, Expert placed 4 out of 7 for quality and 6 out of 7 for empathy. For both quality (P < 0.001) and empathy (P < 0.001), expert-edited LLM responses performed better than expert-created responses. There were time savings for an expert-edited LLM response versus expert-created response (P = 0.02). ChatGPT-4 performed similar to Expert for inappropriate content (P = 0.35), missing content (P = 0.001), extent of possible harm (P = 0.356), and likelihood of possible harm (P = 0.129). Conclusions In this randomized, masked, multicenter study, LLM responses were comparable with experts in terms of quality, empathy, and safety metrics, warranting further exploration of their potential benefits in clinical settings. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of the article.
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Affiliation(s)
| | | | - John J. Chen
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota
| | - Raymond Iezzi
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Sophie J. Bakri
- Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota
| | - Edwin H. Ryan
- Retina Consultants of Minnesota, Edina, Minnesota
- Department of Ophthalmology & Visual Neurosciences, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Peter H. Tang
- Retina Consultants of Minnesota, Edina, Minnesota
- Department of Ophthalmology & Visual Neurosciences, University of Minnesota Medical School, Minneapolis, Minnesota
| | - D. Wilkin. Parke
- Retina Consultants of Minnesota, Edina, Minnesota
- Department of Ophthalmology & Visual Neurosciences, University of Minnesota Medical School, Minneapolis, Minnesota
| | | | - Jayanth Sridhar
- Olive View Medical Center, University of California Los Angeles, Los Angeles, California
| | - David Xu
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Ajay E. Kuriyan
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Yoshihiro Yonekawa
- Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania
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Lu Z, Gong Y, Shen C, Chen H, Zhu W, Liu T, Wu C, Sun M, Su G, Wang X, Wang Y, Ye J, Liu X, Rao H. Portable, intelligent MIECL sensing platform for ciprofloxacin detection using a fast convolutional neural networks-assisted Tb@Lu 2O 3 nanoemitter. Food Chem 2024; 444:138656. [PMID: 38325090 DOI: 10.1016/j.foodchem.2024.138656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/18/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Environmental pollution caused by ciprofloxacin is a major problem of global public health. A machine learning-assisted portable smartphone-based visualized molecularly imprinted electrochemiluminescence (MIECL) sensor was developed for the highly selective and sensitive detection of ciprofloxacin (CFX) in food. To boost the efficiency of electrochemiluminescence (ECL), oxygen vacancies (OVs) enrichment was introduced into the flower-like Tb@Lu2O3 nanoemitter. With the specific recognition reaction between MIP as capture probes and CFX as detection target, the ECL signal significantly decreased. According to, CFX analysis was determined by traditional ECL analyzer detector in the concentration range from 5 × 10-4 to 5 × 102 μmol L-1 with the detection limit (LOD) of 0.095 nmol L-1 (S/N = 3). Analysis of luminescence images using fast electrochemiluminescence judgment network (FEJ-Net) models, achieving portable and intelligent quick analysis of CFX. The proposed MIECL sensor was used for CFX analysis in real meat samples and satisfactory results, as well as efficient selectivity and good stability.
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Affiliation(s)
- Zhiwei Lu
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Yonghui Gong
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Chengao Shen
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Haoran Chen
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Weiling Zhu
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Tao Liu
- College of Information Engineering, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Chun Wu
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Mengmeng Sun
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Gehong Su
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Xianxing Wang
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Yanying Wang
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China
| | - Jianshan Ye
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, PR China
| | - Xin Liu
- College of Food Science and Engineering, Hubei Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan Polytechnic University, Wuhan 430023, PR China.
| | - Hanbing Rao
- College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China.
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Amacher SA, Arpagaus A, Sahmer C, Becker C, Gross S, Urben T, Tisljar K, Sutter R, Marsch S, Hunziker S. Prediction of outcomes after cardiac arrest by a generative artificial intelligence model. Resusc Plus 2024; 18:100587. [PMID: 38433764 PMCID: PMC10906512 DOI: 10.1016/j.resplu.2024.100587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/01/2024] [Accepted: 02/11/2024] [Indexed: 03/05/2024] Open
Abstract
Aims To investigate the prognostic accuracy of a non-medical generative artificial intelligence model (Chat Generative Pre-Trained Transformer 4 - ChatGPT-4) as a novel aspect in predicting death and poor neurological outcome at hospital discharge based on real-life data from cardiac arrest patients. Methods This prospective cohort study investigates the prognostic performance of ChatGPT-4 to predict outcomes at hospital discharge of adult cardiac arrest patients admitted to intensive care at a large Swiss tertiary academic medical center (COMMUNICATE/PROPHETIC cohort study). We prompted ChatGPT-4 with sixteen prognostic parameters derived from established post-cardiac arrest scores for each patient. We compared the prognostic performance of ChatGPT-4 regarding the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and likelihood ratios of three cardiac arrest scores (Out-of-Hospital Cardiac Arrest [OHCA], Cardiac Arrest Hospital Prognosis [CAHP], and PROgnostication using LOGistic regression model for Unselected adult cardiac arrest patients in the Early stages [PROLOGUE score]) for in-hospital mortality and poor neurological outcome. Results Mortality at hospital discharge was 43% (n = 309/713), 54% of patients (n = 387/713) had a poor neurological outcome. ChatGPT-4 showed good discrimination regarding in-hospital mortality with an AUC of 0.85, similar to the OHCA, CAHP, and PROLOGUE (AUCs of 0.82, 0.83, and 0.84, respectively) scores. For poor neurological outcome, ChatGPT-4 showed a similar prediction to the post-cardiac arrest scores (AUC 0.83). Conclusions ChatGPT-4 showed a similar performance in predicting mortality and poor neurological outcome compared to validated post-cardiac arrest scores. However, more research is needed regarding illogical answers for potential incorporation of an LLM in the multimodal outcome prognostication after cardiac arrest.
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Affiliation(s)
- Simon A. Amacher
- Intensive Care Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
- Emergency Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
| | - Armon Arpagaus
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
| | - Christian Sahmer
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
| | - Christoph Becker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
- Emergency Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
| | - Sebastian Gross
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
| | - Tabita Urben
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
| | - Kai Tisljar
- Intensive Care Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
| | - Raoul Sutter
- Intensive Care Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
- Division of Neurophysiology, Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Stephan Marsch
- Intensive Care Medicine, Department of Acute Medical Care, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
| | - Sabina Hunziker
- Medical Communication and Psychosomatic Medicine, University Hospital Basel, Basel, Switzerland
- Medical Faculty, University of Basel, Basel, Switzerland
- Post-Intensive Care Clinic, University Hospital Basel, Basel, Switzerland
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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Lee SE, Hong H, Kim EK. Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography. Eur J Radiol Open 2024; 12:100545. [PMID: 38293282 PMCID: PMC10825593 DOI: 10.1016/j.ejro.2023.100545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/27/2023] [Accepted: 12/29/2023] [Indexed: 02/01/2024] Open
Abstract
Purpose To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.
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Affiliation(s)
| | | | - Eun-Kyung Kim
- Correspondence to: Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gul̥, Yongin-si, Gyeonggi-do, Korea.
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Meng J, Liu JYW, Yang L, Wong MS, Tsang H, Yu B, Yu J, Lam FMH, He D, Yang L, Li Y, Siu GKH, Tyrovolas S, Xie YJ, Man D, Shum DH. An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes. Infect Dis Model 2024; 9:474-482. [PMID: 38404914 PMCID: PMC10885586 DOI: 10.1016/j.idm.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 01/12/2024] [Accepted: 02/03/2024] [Indexed: 02/27/2024] Open
Abstract
An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.
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Affiliation(s)
- Jiahui Meng
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
- Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Justina Yat Wa Liu
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lin Yang
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
- Research Centre of Textiles for Future Fashion, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Man Sing Wong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Hilda Tsang
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Boyu Yu
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Jincheng Yu
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Freddy Man-Hin Lam
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Daihai He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lei Yang
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Yan Li
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Stefanos Tyrovolas
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
- Department of Nutrition and Food Studies, George Mason University, USA
| | - Yao Jie Xie
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - David Man
- Tung Wah College, Hong Kong Special Administrative Region, China
- Mental Health Research Centre, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - David H.K. Shum
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
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Frost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform 2024; 186:105417. [PMID: 38564959 DOI: 10.1016/j.ijmedinf.2024.105417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.
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Affiliation(s)
- Emma Kellie Frost
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Rebecca Bosward
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
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Tobler S. Smart grading: A generative AI-based tool for knowledge-grounded answer evaluation in educational assessments. MethodsX 2024; 12:102531. [PMID: 38204981 PMCID: PMC10776976 DOI: 10.1016/j.mex.2023.102531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Evaluating text-based answers obtained in educational settings or behavioral studies is time-consuming and resource-intensive. Applying novel artificial intelligence tools such as ChatGPT might support the process. Still, currently available implementations do not allow for automated and case-specific evaluations of large numbers of student answers. To counter this limitation, we developed a flexible software and user-friendly web application that enables researchers and educators to use cutting-edge artificial intelligence technologies by providing an interface that combines large language models with options to specify questions of interest, sample solutions, and evaluation instructions for automated answer scoring. We validated the method in an empirical study and found the software with expert ratings to have high reliability. Hence, the present software constitutes a valuable tool to facilitate and enhance text-based answer evaluation.•Generative AI-enhanced software for customizable, case-specific, and automized grading of large amounts of text-based answers.•Open-source software and web application for direct implementation and adaptation.
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Dexter JM, Brubaker LW, Bitler BG, Goff BA, Menon U, Moore KN, Sundaram KM, Walsh CS, Guntupalli SR, Behbakht K. Ovarian cancer think tank: An overview of the current status of ovarian cancer screening and recommendations for future directions. Gynecol Oncol Rep 2024; 53:101376. [PMID: 38590930 PMCID: PMC10999790 DOI: 10.1016/j.gore.2024.101376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/10/2024] Open
Abstract
Early diagnosis and screening of ovarian cancer remain significant challenges to improving patient outcomes. There is an urgent need to implement both established and modern strategies to address the "early detection" conundrum, especially as new research continues to uncover the complexities of the disease. The discussion provided is the result of a unique research conference focused on reviewing early detection modalities and providing insight into future approaches.
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Affiliation(s)
- Julia M. Dexter
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of Colorado, Aurora, CO, USA
| | - Lindsay W. Brubaker
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of Colorado, Aurora, CO, USA
| | - Benjamin G. Bitler
- Department of OB/GYN, Division of Reproductive Sciences, The University of Colorado, Aurora, CO, USA
| | - Barbara A. Goff
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, USA
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Katherine N. Moore
- Stephenson Cancer Center at the University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Karthik M. Sundaram
- Department of Radiology, Hospital of University of Pennsylvania, Philadelphia, PA, USA
| | - Christine S. Walsh
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of Colorado, Aurora, CO, USA
| | - Saketh R. Guntupalli
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of Colorado, Aurora, CO, USA
| | - Kian Behbakht
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, The University of Colorado, Aurora, CO, USA
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Jin Y, Sharifi A, Li Z, Chen S, Zeng S, Zhao S. Carbon emission prediction models: A review. Sci Total Environ 2024; 927:172319. [PMID: 38599410 DOI: 10.1016/j.scitotenv.2024.172319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 03/26/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
Amidst growing concerns over the greenhouse effect, especially its consequential impacts, establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and predict CO2 emission trends is imperative for climate change mitigation. A review of 147 Carbon Emission Prediction Model (CEPM) studies revealed three predominant functions-prediction, optimization, and prediction factor selection. Statistical models, comprising 75 instances, were the most prevalent among prediction models, followed by neural network models at 21.8 %. The consistent rise in neural network model usage, particularly feedforward architectures, was observed from 2019 to 2022. A majority of CEPMs incorporated optimized approaches, with 94.4 % utilizing metaheuristic models. Parameter optimization was the primary focus, followed by structure optimization. Prediction factor selection models, employing Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) for statistical and machine learning models, respectively, filtered factors effectively. Scrutinizing accuracy, pre-optimized CEPMs exhibited varied performance, Root Mean Square Error (RMSE) values spanned from 0.112 to 1635 Mt, while post-optimization led to a notable improvement, the minimum RMSE reached 0.0003 Mt, and the maximum was 95.14 Mt. Finally, we summarized the pros and cons of existing models, classified and counted the factors that influence carbon emissions, clarified the research objectives in CEPM and assessed the applied model evaluation methods and the spatial and temporal scales of existing research.
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Affiliation(s)
- Yukai Jin
- Urban Environmental Science Lab (URBES), Graduate School of Innovation and Practice for Smart Society, Hiroshima University, Higashi-Hiroshima, 739-8529, Japan; School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China
| | - Ayyoob Sharifi
- The IDEC Institute, Hiroshima University, Higashi-Hiroshima, 739-8529, Japan; School of Architecture and Design, Lebanese American University, Beirut, Lebanon.
| | - Zhisheng Li
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China
| | - Sirui Chen
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China
| | - Suzhen Zeng
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China; School of Ocean Engineering and Technology, Sun Yat-sen University, Guangdong, 519000, China
| | - Shanlun Zhao
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangdong, 510006, China
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Cox LA. An AI assistant to help review and improve causal reasoning in epidemiological documents. Glob Epidemiol 2024; 7:100130. [PMID: 38188038 PMCID: PMC10767365 DOI: 10.1016/j.gloepi.2023.100130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/08/2023] [Accepted: 11/24/2023] [Indexed: 01/09/2024] Open
Abstract
Drawing sound causal inferences from observational data is often challenging for both authors and reviewers. This paper discusses the design and application of an Artificial Intelligence Causal Research Assistant (AIA) that seeks to help authors improve causal inferences and conclusions drawn from epidemiological data in health risk assessments. The AIA-assisted review process provides structured reviews and recommendations for improving the causal reasoning, analyses and interpretations made in scientific papers based on epidemiological data. Causal analysis methodologies range from earlier Bradford-Hill considerations to current causal directed acyclic graph (DAG) and related models. AIA seeks to make these methods more accessible and useful to researchers. AIA uses an external script (a "Causal AI Booster" (CAB) program based on classical AI concepts of slot-filling in frames organized into task hierarchies to complete goals) to guide Large Language Models (LLMs), such as OpenAI's ChatGPT or Google's LaMDA (Bard), to systematically review manuscripts and create both (a) recommendations for what to do to improve analyses and reporting; and (b) explanations and support for the recommendations. Review tables and summaries are completed systematically by the LLM in order. For example, recommendations for how to state and caveat causal conclusions in the Abstract and Discussion sections reflect previous analyses of the Study Design and Data Analysis sections. This work illustrates how current AI can contribute to reviewing and providing constructive feedback on research documents. We believe that such AI-assisted review shows promise for enhancing the quality of causal reasoning and exposition in epidemiological studies. It suggests the potential for effective human-AI collaboration in scientific authoring and review processes.
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Affiliation(s)
- Louis Anthony Cox
- Cox Associates, Entanglement, and University of Colorado, United States of America
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Pingitore A, Zhang C, Vassalle C, Ferragina P, Landi P, Mastorci F, Sicari R, Tommasi A, Zavattari C, Prencipe G, Sîrbu A. Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease. Int J Cardiol 2024; 404:131981. [PMID: 38527629 DOI: 10.1016/j.ijcard.2024.131981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/13/2024] [Accepted: 03/17/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Machine learning (ML) employs algorithms that learn from data, building models with the potential to predict events by aggregating a large number of variables and assessing their complex interactions. The aim of this study is to assess ML potential in identifying patients with ischemic heart disease (IHD) at high risk of cardiac death (CD). METHODS 3987 (mean age 68 ± 11) hospitalized IHD patients were enrolled. We implemented and compared various ML models and their combination into ensembles. Model output constitutes a new ML indicator to be employed for stratification. Primary variable importance was assessed with ablation tests. RESULTS An ensemble classifier combining three ML models achieved the best performance to predict CD (AUROC of 0.830, F1-macro of 0.726). ML indicator use through Cox survival analysis outperformed the 18 variables individually, producing a better stratification compared to standard multivariate analysis (improvement of ∼20%). Patients in the low risk group defined through ML indicator had a significantly higher survival (88.8% versus 29.1%). The main variables identified were Dyslipidemia, LVEF, Previous CABG, Diabetes, Previous Myocardial Infarction, Smoke, Documented resting or exertional ischemia, with an AUROC of 0.791 and an F1-score of 0.674, lower than that of 18 variables. Both code and clinical data are freely available with this article. CONCLUSION ML may allow a faster, low-cost and reliable evaluation of IHD patient prognosis by inclusion of more predictors and identification of those more significant, improving outcome prediction towards the development of precision medicine in this clinical field.
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Affiliation(s)
| | - Chenxiang Zhang
- Computer Science Department, University of Pisa, Pisa, Italy
| | | | - Paolo Ferragina
- Computer Science Department, University of Pisa, Pisa, Italy
| | | | | | - Rosa Sicari
- Clinical Physiology Institute, CNR, Pisa, Italy
| | | | | | | | - Alina Sîrbu
- Computer Science Department, University of Pisa, Pisa, Italy
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Qin Y, Qin X, Zhang J, Guo X. Artificial intelligence: The future for multimodality imaging of right ventricle. Int J Cardiol 2024; 404:131970. [PMID: 38490268 DOI: 10.1016/j.ijcard.2024.131970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 03/17/2024]
Abstract
The crucial pathophysiological and prognostic roles of the right ventricle in various diseases have been well-established. Nonetheless, conventional cardiovascular imaging modalities are frequently associated with intrinsic limitations when evaluating right ventricular (RV) morphology and function. The integration of artificial intelligence (AI) in multimodality imaging presents a promising avenue to circumvent these obstacles, paving the way for future fully automated imaging paradigms. This review aimed to address the current challenges faced by clinicians and researchers in integrating RV imaging and AI technology, to provide a comprehensive overview of the current applications of AI in RV imaging, and to offer insights into future directions, opportunities, and potential challenges in this rapidly advancing field.
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Affiliation(s)
- Yuhan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaohan Qin
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Jing Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xiaoxiao Guo
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Makhlouf E, Alenezi A, Shokr EA. Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design. Nurse Education Today 2024; 137:106159. [PMID: 38493588 DOI: 10.1016/j.nedt.2024.106159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 03/05/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Chatbots have gained popularity in the healthcare industry due to their ability to provide prompt and accurate responses to a wide range of inquiries. This has been particularly beneficial for nurses who often require quick access to information and may have questions related to patient care. PURPOSE To evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program. METHODS The study utilized a Quasi-experimental design and collected data from a purposive sample of 73 nurses using Google Forms. The tools used in the study included 1) a structured questionnaire for nurses (a) Demographic data (b) Nurses' knowledge regarding chatbots, 2) Nurses' knowledge about artificial intelligence, 3) Nurses' perception of the application of chatbots in nursing, and 4) Nurses' opinions about the use of nursing chatbots or traditional methods of education. RESULTS There was a highly statistically significant improvement in nurses' knowledge-based chatbot systems post-intervention (p = 0.001). CONCLUSION Integrating an artificial intelligence chatbot system into a nursing training program provides nurses with easy access to reliable and evidence-based knowledge. The chatbot offers immediate answers, explanations, and up-to-date resources, empowering nurses to make informed decisions, stay updated, and served as a communication platform connecting nurses through a common language and enhance their practice. RECOMMENDATION Incorporating a knowledge-based chatbot system into a nursing training program. Furthermore, further research should be conducted to explore the long-term impact of chatbot systems on nursing practices and education.
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Affiliation(s)
- Entesar Makhlouf
- Maternity and Child Health Nursing Department, College of Nursing, Al Dawadmi, Shaqra University, 11911, Saudi Arabia.
| | - Atallah Alenezi
- Mental Health Nursing, College of Applied Medical Sciences, Shaqra University, Al Dawadmi 11911, Saudi Arabia.
| | - Eman A Shokr
- Community Health Nursing, Faculty of Nursing, Menoufia University, Egypt.
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Stogiannos N, Litosseliti L, O'Regan T, Scurr E, Barnes A, Kumar A, Malik R, Pogose M, Harvey H, McEntee MF, Malamateniou C. Black box no more: A cross-sectional multi-disciplinary survey for exploring governance and guiding adoption of AI in medical imaging and radiotherapy in the UK. Int J Med Inform 2024; 186:105423. [PMID: 38531254 DOI: 10.1016/j.ijmedinf.2024.105423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future. METHODS This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions. RESULTS Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.
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Affiliation(s)
- Nikolaos Stogiannos
- Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece.
| | - Lia Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - Tracy O'Regan
- The Society and College of Radiographers, London, UK.
| | | | - Anna Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | | | | | | | | | - Mark F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - Christina Malamateniou
- Department of Radiography, City, University of London, UK; European Society of Medical Imaging Informatics, Vienna, Austria.
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Zhou Y, Chen SJ. Advances in machine-learning approaches to RNA-targeted drug design. Artif Intell Chem 2024; 2:100053. [PMID: 38434217 PMCID: PMC10904028 DOI: 10.1016/j.aichem.2024.100053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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Kim S, Kim K, Wonjeong Jo C. Accuracy of a large language model in distinguishing anti- and pro-vaccination messages on social media: The case of human papillomavirus vaccination. Prev Med Rep 2024; 42:102723. [PMID: 38659997 PMCID: PMC11039308 DOI: 10.1016/j.pmedr.2024.102723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
Objective Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomavirus (HPV) vaccination. Methods Messages related to HPV vaccination were collected from social media supporting different message formats: Facebook (long format) and Twitter (short format). A selection of 1,000 human-evaluated messages was input into the LLM, which generated multiple response instances containing its classification results. Accuracy was measured for each message as the level of concurrence between human and machine decisions, ranging between 0 and 1. Results Average accuracy was notably high when 20 response instances were used to determine the machine decision of each message: .882 (SE = .021) and .750 (SE = .029) for anti- and pro-vaccination long-form; .773 (SE = .027) and .723 (SE = .029) for anti- and pro-vaccination short-form, respectively. Using only three or even one instance did not lead to a severe decrease in accuracy. However, for long-form messages, the language model exhibited significantly lower accuracy in categorizing pro-vaccination messages than anti-vaccination ones. Conclusions ChatGPT shows potential in analyzing public opinions on HPV vaccination using social media content. However, understanding the characteristics and limitations of a language model within specific public health contexts remains imperative.
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Affiliation(s)
- Soojong Kim
- Department of Communication, University of California Davis, United States
| | - Kwanho Kim
- Department of Communication, Cornell University, United States
| | - Claire Wonjeong Jo
- Department of Communication, University of California Davis, United States
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50
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Occhipinti JA, Prodan A, Hynes W, Eyre HA, Schulze A, Ujdur G, Tanner M. Navigating a stable transition to the age of intelligence: A mental wealth perspective. iScience 2024; 27:109645. [PMID: 38638562 PMCID: PMC11024996 DOI: 10.1016/j.isci.2024.109645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
In the grand narrative of technological evolution, we are transitioning from the "Age of Information" to the "Age of Intelligence." Rapid advancements in generative artificial intelligence (AI) are set to reshape society, revolutionize industries, and change the nature of work, challenging our traditional understanding of the dynamics of the economy and its relationship with human productivity and societal prosperity. As we brace for this transformative shift, promising advancements in healthcare, education, productivity, and more, there are concerns of large-scale job loss, mental health repercussions, and risks to social stability and democracy. This paper proposes the concept of Mental Wealth as an action framework that supports nations to proactively position themselves for a smooth transition to the Age of Intelligence while fostering economic and societal prosperity.
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Affiliation(s)
- Jo-An Occhipinti
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Mental Wealth Initiative, University of Sydney, Camperdown, NSW, Australia
- Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW, Australia
| | - Ante Prodan
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Mental Wealth Initiative, University of Sydney, Camperdown, NSW, Australia
- Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW, Australia
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW, Australia
| | - William Hynes
- The World Bank, Paris, France
- Santa Fe Institute, Santa Fe, NM, USA
| | - Harris A. Eyre
- Brain Capital Alliance, San Francisco, CA, USA
- Baker Institute for Public Policy, Rice University, Houston, TX, USA
- Meadows Mental Health Policy Institute, Dallas, TX, USA
| | | | - Goran Ujdur
- Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Mental Wealth Initiative, University of Sydney, Camperdown, NSW, Australia
- Computer Simulation & Advanced Research Technologies (CSART), Sydney, NSW, Australia
| | - Marcel Tanner
- Swiss Academies of Arts and Sciences, Bern, Switzerland
- Swiss Tropical and Public Health Institute, Allschwil, Basel, Switzerland
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