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1.
J Transl Med ; 22(1): 616, 2024 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-38961396

RESUMO

Fibrosis is a pathological process involving the abnormal deposition of connective tissue, resulting from improper tissue repair in response to sustained injury caused by hypoxia, infection, or physical damage. It can impact any organ, leading to their dysfunction and eventual failure. Additionally, tissue fibrosis plays an important role in carcinogenesis and the progression of cancer.Early and accurate diagnosis of organ fibrosis, coupled with regular surveillance, is essential for timely disease-modifying interventions, ultimately reducing mortality and enhancing quality of life. While extensive research has already been carried out on the topics of aberrant wound healing and fibrogenesis, we lack a thorough understanding of how their relationship reveals itself through modern imaging techniques.This paper focuses on fibrosis of the genito-urinary system, detailing relevant imaging technologies used for its detection and exploring future directions.


Assuntos
Fibrose , Humanos , Sistema Urogenital/diagnóstico por imagem , Sistema Urogenital/patologia , Radiologia
2.
BMC Med Educ ; 24(1): 740, 2024 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-38982410

RESUMO

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.


Assuntos
Inteligência Artificial , Internato e Residência , Radiologia , Feminino , Humanos , Masculino , Competência Clínica , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/diagnóstico , Nódulos Pulmonares Múltiplos/diagnóstico por imagem , Radiologia/educação , Nódulo Pulmonar Solitário/diagnóstico por imagem , Nódulo Pulmonar Solitário/diagnóstico , Estudantes de Medicina
4.
Front Endocrinol (Lausanne) ; 15: 1372397, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-39015174

RESUMO

Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact: Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.


Assuntos
Inteligência Artificial , Diagnóstico por Computador , Nódulo da Glândula Tireoide , Humanos , Nódulo da Glândula Tireoide/diagnóstico , Nódulo da Glândula Tireoide/diagnóstico por imagem , Feminino , Masculino , Diagnóstico por Computador/métodos , Competência Clínica , Adulto , Ultrassonografia/métodos , Radiologia/educação , Curva ROC , Internato e Residência/métodos , Pessoa de Meia-Idade
8.
9.
Radiology ; 311(3): e232653, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38888474

RESUMO

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.


Assuntos
Inteligência Artificial , Sistemas de Informação em Radiologia , Integração de Sistemas , Fluxo de Trabalho , Radiologia/normas , Sistemas de Informação em Radiologia/normas
10.
Sci Data ; 11(1): 688, 2024 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-38926396

RESUMO

Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.


Assuntos
Imagem Multimodal , Radiologia , Humanos , Processamento de Imagem Assistida por Computador , Unified Medical Language System
11.
J Comput Biol ; 31(6): 486-497, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38837136

RESUMO

Automatic radiology medical report generation is a necessary development of artificial intelligence technology in the health care. This technology serves to aid doctors in producing comprehensive diagnostic reports, alleviating the burdensome workloads of medical professionals. However, there are some challenges in generating radiological reports: (1) visual and textual data biases and (2) long-distance dependency problem. To tackle these issues, we design a visual recalibration and gating enhancement network (VRGE), which composes of the visual recalibration module and the gating enhancement module (gating enhancement module, GEM). Specifically, the visual recalibration module enhances the recognition of abnormal features in lesion areas of medical images. The GEM dynamically adjusts the contextual information in the report by introducing gating mechanisms, focusing on capturing professional medical terminology in medical text reports. We have conducted sufficient experiments on the public datasets of IU X-Ray to illustrate that the VRGE outperforms existing models.


Assuntos
Inteligência Artificial , Humanos , Radiologia/métodos , Algoritmos
12.
Sci Rep ; 14(1): 13218, 2024 06 08.
Artigo em Inglês | MEDLINE | ID: mdl-38851825

RESUMO

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.


Assuntos
Inteligência Artificial , Assistência Centrada no Paciente , Humanos , Imageamento por Ressonância Magnética/métodos , Radiologia/métodos , Masculino , Feminino , Pessoa de Meia-Idade , Adulto , Fluxo de Trabalho , Idoso
13.
BMC Med Educ ; 24(1): 662, 2024 Jun 14.
Artigo em Inglês | MEDLINE | ID: mdl-38877548

RESUMO

BACKGROUND: Good communication is an important professional attribute for radiologists. However, explorations of communication education and their outcomes in radiology residents are sparse. This scoping review aims to evaluate the existing literature on communication education for radiology residents, identify gaps in current practices, and suggest directions for future studies. METHODS: A scoping review following the six-step approach of Arksey and O'Malley was undertaken. We searched through PubMed, Embase, ERIC, and Web of Science databases, focusing on communication education in radiology residents. RESULTS: Sixteen of the 3096 identified articles were included in the analysis. Most studies (13/16) originated from the United States. The studies varied in study design, including quantitative, qualitative and mixed-methods approaches. The sample sizes of most studies were small to moderate, with more than half of the studies had fewer than 30 participants. The identified studies predominantly focused on communication with patients and healthcare professionals. The need for communication education, the efficacy of specific communication education programs, and the capability of some assessment tools for evaluating residents' communication skills were investigated. CONCLUSIONS: This scoping review reveals the gap between the need for communication education and the lack of comprehensive education programs in radiology residents globally. Future studies should develop tailored interventions and use reliable assessment tools, engaging more participants with extended follow-up periods, and expand the scope of communication training to include all relevant stakeholders.


Assuntos
Comunicação , Internato e Residência , Radiologia , Humanos , Radiologia/educação , Competência Clínica , Relações Médico-Paciente , Currículo
14.
Radiographics ; 44(7): e230059, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38843094

RESUMO

Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.


Assuntos
Viés , Cognição , Erros de Diagnóstico , Humanos , Erros de Diagnóstico/prevenção & controle , Radiologia , Radiologistas
15.
Radiography (Lond) ; 30(4): 1210-1218, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38905765

RESUMO

INTRODUCTION: Evidence based practice relies on availability of research evidence mostly through peer-reviewed journal publications. No consensus currently exists on the best hierarchy of research evidence, often categorised by the adopted research designs. Analysing the prevalent research designs in radiography professional journals is one vital step in considering an evidence hierarchy specific to the radiography profession and this forms the aim of this study. METHODS: Bibliometric data of publications in three Radiography professional journals within a 10-year period were extracted. The Digital Object Identifier were used to locate papers on publishers' websites and obtain relevant data for analysis. Descriptive analysis using frequencies and percentages were used to represent data while Chi-square was used to analyse relationship between categorical variables. RESULTS: 1830 articles met the pre-set inclusion criteria. Quantitative descriptive studies were the most published design (26.6%) followed by non-RCT experimental studies (18.7%), while Randomised Controlled Trials (RCT) were the least published (1.0%). Systematic reviews (42.9%) showed the highest average percentage increase within the 10-year period, however RCTs showed no net increase. Single-centre studies predominated among experimental studies (RCT = 88.9%; Non-RCT = 95%). Author collaboration across all study designs was notable, with RCTs showing the most (100%). Quantitative and qualitative studies comparatively had similar number of citations when publication numbers were matched. Quantitative descriptive studies had the highest cumulative citations while RCTs had the least. CONCLUSION: There is a case to advocate for more study designs towards the peak of evidence hierarchies such as systematic reviews and RCT. Radiography research should be primarily designed to answer pertinent questions and improve the validity of the profession's evidence base. IMPLICATION FOR PRACTICE: The evidence presented can encourage the adoption of the research designs that enhances radiography profession's evidence base.


Assuntos
Bibliometria , Publicações Periódicas como Assunto , Radiologia , Projetos de Pesquisa , Humanos , Radiografia/estatística & dados numéricos
17.
Korean J Radiol ; 25(7): 597-599, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38942452
19.
Korean J Radiol ; 25(7): 613-622, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38942455

RESUMO

OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). MATERIALS AND METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. CONCLUSION: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.


Assuntos
Inteligência Artificial , Sociedades Médicas , Humanos , República da Coreia , Inquéritos e Questionários , Radiologia , Software
20.
Korean J Radiol ; 25(7): 595-596, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38942451
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