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3.
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
4.
Arch. argent. pediatr ; 122(3): e202303026, jun. 2024. ilus
Artigo em Inglês, Espanhol | LILACS, BINACIS | ID: biblio-1554938

RESUMO

El maltrato infantil es definido por la Organización Mundial de la Salud (OMS) como "el abuso y la desatención que sufren los niños menores de 18 años. Incluye todo tipo de maltrato físico y/o emocional […] que resulte en un daño real o potencial para la salud, la supervivencia, el desarrollo o la dignidad del niño". Al examinar los rastros corporales del maltrato físico, siguiendo los mecanismos de lesión más frecuentemente implicados, es posible detectar patrones radiológicos típicos. La evaluación imagenológica del hueso en reparación permite inferir cronologías para correlacionar con los datos obtenidos en la anamnesis. Los profesionales de la salud deben detectar oportunamente lesiones radiológicas sospechosas y activar de forma temprana el resguardo del menor. Nuestro propósito es realizar una revisión sobre las publicaciones recientes referidas al estudio imagenológico en niños de quienes se sospeche que puedan ser víctimas de violencia física.


The World Health Organization (WHO) defines child maltreatment as "the abuse and neglect that occurs to children under 18 years of age. It includes all types of physical and/or emotional ill-treatment [...], which results in actual or potential harm to the child's health, survival, development or dignity." By examining the bodily traces of physical abuse, following the most frequently involved mechanisms of injury, it is possible to identify typical radiological patterns. The imaging studies of the bone under repair allows inferring a timeline that may be correlated to the data obtained during history taking. Health care providers should detect suspicious radiological lesions in a timely manner and promptly activate the safeguarding of the child. Our objective was to review recent publications on the imaging studies of children suspected of being victims of physical violence.


Assuntos
Humanos , Pré-Escolar , Criança , Adolescente , Maus-Tratos Infantis/psicologia , Violência , Radiologistas
6.
Clin Radiol ; 79(7): 479-484, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38729906

RESUMO

This narrative review describes our experience of working with Doug Altman, the most highly cited medical statistician in the world. Doug was particularly interested in diagnostics, and imaging studies in particular. We describe how his insights helped improve our own radiological research studies and we provide advice for other researchers hoping to improve their own research practice.


Assuntos
Radiologia , Humanos , História do Século XX , História do Século XXI , Radiologistas
8.
Aust Health Rev ; 48(3): 299-311, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38692648

RESUMO

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.


Assuntos
Inteligência Artificial , Neoplasias da Mama , Detecção Precoce de Câncer , Mamografia , Adulto , Feminino , Humanos , Pessoa de Meia-Idade , Austrália , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/psicologia , Detecção Precoce de Câncer/métodos , Detecção Precoce de Câncer/psicologia , Mamografia/métodos , Radiologistas/psicologia , Inquéritos e Questionários
10.
Radiologia (Engl Ed) ; 66(2): 132-154, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38614530

RESUMO

80% of renal carcinomas (RC) are diagnosed incidentally by imaging. 2-4% of "sporadic" multifocality and 5-8% of hereditary syndromes are accepted, probably with underestimation. Multifocality, young age, familiar history, syndromic data, and certain histologies lead to suspicion of hereditary syndrome. Each tumor must be studied individually, with a multidisciplinary evaluation of the patient. Nephron-sparing therapeutic strategies and a radioprotective diagnostic approach are recommended. Relevant data for the radiologist in major RC hereditary syndromes are presented: von-Hippel-Lindau, Chromosome-3 translocation, BRCA-associated protein-1 mutation, RC associated with succinate dehydrogenase deficiency, PTEN, hereditary papillary RC, Papillary thyroid cancer- Papillary RC, Hereditary leiomyomatosis and RC, Birt-Hogg-Dubé, Tuberous sclerosis complex, Lynch, Xp11.2 translocation/TFE3 fusion, Sickle cell trait, DICER1 mutation, Hereditary hyperparathyroidism and jaw tumor, as well as the main syndromes of Wilms tumor predisposition. The concept of "non-hereditary" familial RC and other malignant and benign entities that can present as multiple renal lesions are discussed.


Assuntos
Carcinoma de Células Renais , Neoplasias Renais , Humanos , Neoplasias Renais/diagnóstico por imagem , Neoplasias Renais/genética , Radiologistas , Ribonuclease III , RNA Helicases DEAD-box
11.
J Breast Imaging ; 6(3): 246-253, 2024 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-38655858

RESUMO

OBJECTIVE: To evaluate the association of mammographic, radiologist, and patient factors on BI-RADS 3 assessment at diagnostic mammography in patients recalled from screening mammography. METHODS: This Institutional Review Board-approved retrospective study of consecutive unique diagnostic mammography examinations in asymptomatic patients recalled from screening mammography March 5, 2014, to December 31, 2019, was conducted in a single large United States health care institution. Mammographic features (mass, calcification, distortion, asymmetry), breast density, prior examination, and BI-RADS assessment were extracted from reports by natural language processing. Patient age, race, and ethnicity were extracted from the electronic health record. Radiologist years in practice, recall rate, and number of interpreted diagnostic mammograms were calculated. A mixed effect logistic regression model evaluated factors associated with likelihood of BI-RADS 3 compared with other BI-RADS assessments. RESULTS: A total of 12 080 diagnostic mammography examinations were performed during the study period, yielding 2010 (16.6%) BI-RADS 3 and 10 070 (83.4%) other BI-RADS assessments. Asymmetry (odds ratio [OR] = 6.49, P <.001) and calcification (OR = 5.59, P <.001) were associated with increased likelihood of BI-RADS 3 assessment; distortion (OR = 0.20, P <.001), dense breast parenchyma (OR = 0.82, P <.001), prior examination (OR = 0.63, P = .01), and increasing patient age (OR = 0.99, P <.001) were associated with decreased likelihood. Mass, patient race or ethnicity, and radiologist factors were not significantly associated with BI-RADS 3 assessment. Malignancy rate for BI-RADS 3 lesions was 1.6%. CONCLUSION: Asymmetry and calcifications had an increased likelihood of BI-RADS 3 assessment at diagnostic evaluation with low likelihood of malignancy, while radiologist features had no association.


Assuntos
Neoplasias da Mama , Mamografia , Humanos , Mamografia/métodos , Feminino , Estudos Retrospectivos , Pessoa de Meia-Idade , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico , Idoso , Adulto , Radiologistas/estatística & dados numéricos , Densidade da Mama , Mama/diagnóstico por imagem , Mama/patologia
12.
Emerg Radiol ; 31(3): 429-434, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38581613

RESUMO

Overnight radiology (ONR) is necessary for providing timely patient care but poses unique professional and personal challenges to the radiologists. Maintaining a sustainable, long-term overnight radiology program hinges on the retention of radiologists who grasp the institutional workflow and can adeptly navigate inherent disruptions while consistently delivering high-quality patient care. Design of radiology shifts can significantly impact the performance and well-being of radiologists, with downstream implications for patient care and risk management. We provide a narrative review of literature to make recommendations for optimally designing ONR shifts, with a focus on professional and personal challenges pertinent to overnight radiologists and system-based risk mitigation strategies.


Assuntos
Serviço Hospitalar de Radiologia , Humanos , Serviço Hospitalar de Radiologia/organização & administração , Fluxo de Trabalho , Radiologistas , Admissão e Escalonamento de Pessoal , Gestão de Riscos
13.
Eur J Radiol ; 175: 111462, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38608500

RESUMO

The integration of AI in radiology raises significant legal questions about responsibility for errors. Radiologists fear AI may introduce new legal challenges, despite its potential to enhance diagnostic accuracy. AI tools, even those approved by regulatory bodies like the FDA or CE, are not perfect, posing a risk of failure. The key issue is how AI is implemented: as a stand-alone diagnostic tool or as an aid to radiologists. The latter approach could reduce undesired side effects. However, it's unclear who should be held liable for AI failures, with potential candidates ranging from engineers and radiologists involved in AI development to companies and department heads who integrate these tools into clinical practice. The EU's AI Act, recognizing AI's risks, categorizes applications by risk level, with many radiology-related AI tools considered high risk. Legal precedents in autonomous vehicles offer some guidance on assigning responsibility. Yet, the existing legal challenges in radiology, such as diagnostic errors, persist. AI's potential to improve diagnostics raises questions about the legal implications of not using available AI tools. For instance, an AI tool improving the detection of pediatric fractures could reduce legal risks. This situation parallels innovations like car turn signals, where ignoring available safety enhancements could lead to legal problems. The debate underscores the need for further research and regulation to clarify AI's role in radiology, balancing innovation with legal and ethical considerations.


Assuntos
Inteligência Artificial , Responsabilidade Legal , Radiologia , Humanos , Radiologia/legislação & jurisprudência , Radiologia/ética , Inteligência Artificial/legislação & jurisprudência , Erros de Diagnóstico/legislação & jurisprudência , Erros de Diagnóstico/prevenção & controle , Radiologistas/legislação & jurisprudência
14.
J Breast Imaging ; 6(3): 271-276, 2024 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-38625712

RESUMO

OBJECTIVE: The objectives of this Society of Breast Imaging (SBI)-member survey study were to assess the current imaging patterns for evaluation of symptomatic and asymptomatic breast implant integrity, including modalities used and imaging intervals. METHODS: A 12-question survey assessing the frequency of imaging modalities used to evaluate implant integrity, approximate number of breast implant integrity studies requested per month, intervals of integrity studies, and referring provider and radiology practice characteristics was distributed to members of the SBI. RESULTS: The survey response rate was 7.6% (143/1890). Of responding radiologists, 54.2% (77/142) were in private, 29.6% (42/142) in academic, and 16.2% (23/142) in hybrid practice. Among respondents, the most common initial examination for evaluating implant integrity was MRI without contrast at 53.1% (76/143), followed by handheld US at 46.9% (67/143). Of respondents using US, 67.4% (91/135) also evaluated the breast tissue for abnormalities. Among respondents, 34.1% (46/135) reported being very confident or confident in US for diagnosing implant rupture. There was a range of reported intervals for performing implant integrity studies: 39.1% (43/110) every 2-3 years, 26.4% (29/110) every 4-5 years, 15.5% (17/110) every 6-10 years, and 19.1% (21/110) every 10 years. CONCLUSION: For assessment of implant integrity, the majority of respondents (53.2%, 76/143) reported MRI as initial imaging test. US is less costly, but the minority of respondents (34.1%, 46/135) had confidence in US performance. Also, the minority of respondents (39.1%, 43/110) performed implant integrity evaluations every 2-3 years per the FDA recommendations for asymptomatic surveillance.


Assuntos
Implantes de Mama , Imageamento por Ressonância Magnética , Padrões de Prática Médica , Humanos , Feminino , Imageamento por Ressonância Magnética/estatística & dados numéricos , Padrões de Prática Médica/estatística & dados numéricos , Inquéritos e Questionários , Radiologistas/estatística & dados numéricos , Sociedades Médicas , Ultrassonografia Mamária/estatística & dados numéricos , Falha de Prótese
15.
Sci Rep ; 14(1): 8372, 2024 04 10.
Artigo em Inglês | MEDLINE | ID: mdl-38600311

RESUMO

Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.


Assuntos
Radiologia , Fraturas das Costelas , Humanos , Criança , Pré-Escolar , Fraturas das Costelas/diagnóstico por imagem , Fraturas das Costelas/etiologia , Radiografia , Redes Neurais de Computação , Radiologistas , Estudos Retrospectivos , Sensibilidade e Especificidade
16.
Radiographics ; 44(5): e230153, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38602868

RESUMO

RASopathies are a heterogeneous group of genetic syndromes caused by germline mutations in a group of genes that encode components or regulators of the Ras/mitogen-activated protein kinase (MAPK) signaling pathway. RASopathies include neurofibromatosis type 1, Legius syndrome, Noonan syndrome, Costello syndrome, cardiofaciocutaneous syndrome, central conducting lymphatic anomaly, and capillary malformation-arteriovenous malformation syndrome. These disorders are grouped together as RASopathies based on our current understanding of the Ras/MAPK pathway. Abnormal activation of the Ras/MAPK pathway plays a major role in development of RASopathies. The individual disorders of RASopathies are rare, but collectively they are the most common genetic condition (one in 1000 newborns). Activation or dysregulation of the common Ras/MAPK pathway gives rise to overlapping clinical features of RASopathies, involving the cardiovascular, lymphatic, musculoskeletal, cutaneous, and central nervous systems. At the same time, there is much phenotypic variability in this group of disorders. Benign and malignant tumors are associated with certain disorders. Recently, many institutions have established multidisciplinary RASopathy clinics to address unique therapeutic challenges for patients with RASopathies. Medications developed for Ras/MAPK pathway-related cancer treatment may also control the clinical symptoms due to an abnormal Ras/MAPK pathway in RASopathies. Therefore, radiologists need to be aware of the concept of RASopathies to participate in multidisciplinary care. As with the clinical manifestations, imaging features of RASopathies are overlapping and at the same time diverse. As an introduction to the concept of RASopathies, the authors present major representative RASopathies, with emphasis on their imaging similarities and differences. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.


Assuntos
Síndrome de Costello , Displasia Ectodérmica , Cardiopatias Congênitas , Síndrome de Noonan , Recém-Nascido , Humanos , Síndrome de Noonan/diagnóstico por imagem , Síndrome de Noonan/genética , Cardiopatias Congênitas/diagnóstico por imagem , Cardiopatias Congênitas/genética , Displasia Ectodérmica/diagnóstico por imagem , Displasia Ectodérmica/genética , Radiologistas
17.
Radiologia (Engl Ed) ; 66(2): 121-131, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38614529

RESUMO

INTRODUCTION: There are gender inequalities in all fields, including radiology. Although the situation is improving, the presence of radiologists in leadership positions continues to be a minority. The objective of this article is to analyse the situation of women in the spanish radiology, comparing it with Europe and the United States. MATERIALS AND METHODS: We selected the years 2000-2022 as reference period to make a comparison with feminization data throughout history. In addition, relevant specific data from the just begun 2023 were also included. The variables in which we investigated feminization were the following: medical students, medical graduates, radiology residents and specialists, section chiefs, department chairs, radiology residency programme directors, radiology university professors, presidents of the main radiological entities and societies in Spain, Europe and the United States, recipients of the main awards given by these radiological societies and chief editors of their journals. In order to perform this analysis we conducted an in-depth bibliographic research, we contacted the radiological societies of Spain, Europe and the USA and we carried out a survey in the main Spanish radiology departments. RESULTS: The female presence in radiology decreases as we rise to leadership positions, a situation that is patent in Spain, Europe and the US, comparison that will be analysed in depth throughout the article. In Spanish hospitals in 2021 there were 58.1% female radiology residents, 55% female radiologists, 42.9% female section chiefs and 24.4% female department chairs. In SERAM's history there have been 10% female presidents, 22% female gold medallists and 5% female editors-in-chief. If we analyse data from 2000 to 2023, female presidents reach 32% and female gold medallists 31%. CONCLUSIONS: Although gender inequality is declining, in radiology women continue to be underrepresented in leadership positions. Work must be done in order to build a diverse and inclusive profession that reflects demographic reality.


Assuntos
Feminização , Radiologia , Feminino , Humanos , Masculino , Espanha , Radiografia , Radiologistas
18.
Radiology ; 311(1): e232714, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38625012

RESUMO

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.


Assuntos
Radiologia , Humanos , Estudos Retrospectivos , Radiografia , Radiologistas , Confusão
19.
Breast Cancer Res ; 26(1): 68, 2024 Apr 22.
Artigo em Inglês | MEDLINE | ID: mdl-38649889

RESUMO

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.


Assuntos
Inteligência Artificial , Densidade da Mama , Neoplasias da Mama , Detecção Precoce de Câncer , Mamografia , Radiologistas , Humanos , Feminino , Neoplasias da Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/patologia , Neoplasias da Mama/epidemiologia , Mamografia/métodos , Adulto , Pessoa de Meia-Idade , Detecção Precoce de Câncer/métodos , Estudos Retrospectivos , República da Coreia/epidemiologia , Curva ROC , Mama/diagnóstico por imagem , Mama/patologia , Algoritmos , Programas de Rastreamento/métodos , Sensibilidade e Especificidade
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