Optimizing communication between the radiologist and the general practitioner

Jans, Lennart; Bosmans, Jan; Verstraete, K; Achten, Rik

doi:10.5334/jbr-btr.472

Cited by 8 publications

(4 citation statements)

References 6 publications

(14 reference statements)

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“…As mentioned earlier, we base our framework on multi-agent decision-making processes in a clinical setting. In many applications, this can be considered information passing between two agents – a radiologist and a general practitioner [ 33 ]. While the radiologist specializing in imaging diagnosis is in charge of producing radiology reports, the general practitioner relies on various modalities including the radiologic findings to forecast the severity of a certain disease.…”

Section: Methodsmentioning

confidence: 99%

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data

Nguyen,

Blaschko,

Saarakkala

et al. 2024

IEEE Trans. Med. Imaging

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Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two agents -a radiologist and a general practitioner -we predict prognosis with two transformer-based components that share information with each other. The first transformer in this framework aims to analyze the imaging data, and the second one leverages its internal states as inputs, also fusing them with auxiliary clinical data. The temporal nature of the problem is modeled within the transformer states, allowing us to treat the forecasting problem as a multi-task classification, for which we propose a novel loss. We show the effectiveness of our approach in predicting the development of structural knee osteoarthritis changes and forecasting Alzheimer's disease clinical status directly from raw multi-modal data. The proposed method outperforms multiple state-of-the-art baselines with respect to performance and calibration, both of which are needed for realworld applications. An open-source implementation of our method is made publicly available at https://github. com/Oulu-IMEDS/CLIMATv2.

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Section: Methodsmentioning

confidence: 99%

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data

Nguyen,

Blaschko,

Saarakkala

et al. 2024

IEEE Trans. Med. Imaging

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“…En las publicaciones anglosajonas puede hallarse un sinnúmero de opiniones de expertos que emiten recomendaciones sobre la forma de presentar un informe radiológico 1,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] , así como la importancia de una comunicación entre el médico tratante y el médico radiólogo. Siempre es necesario entregar información clínica al solicitar estudios radiológicos o de imagen 19 ; a través del informe radiológico el médico radiólogo "piensa como clínico" y ofrece respuestas clínicas basadas en su interpretación de las imágenes con base en guías de práctica clínica y formatos prestablecidos para la elaboración de solicitudes de estudios de imágenes 20 , sin perder de vista la interacción entre el médico tratante y el médico radiólogo 21,22 ; éste es un requisito para un entendimiento mutuo, competencia profesional reconocida y confianza mutua, factores clave en la comunicación interprofesional 20,23 . Por ello deben buscarse formas de into account to be able to interpret radiological images?, What findings in the description of an image should we promptly communicate to the treating physician?…”

Section: Objetivosunclassified

Aprendizaje radiológico basado en problema clínico específico: parte 2, ¿cómo se elabora un informe radiológico?

Motta-Ramírez¹

2021

ARM

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“…Electronic healthcare platforms alongside direct communication have been shown to enable all clinicians to deliver better patient service. 8 Implementation of such systems at national scale would require input and financial support on a clinical commissioning group level.…”

Section: Ir(me)r Have Established the 'As Low As Reasonablymentioning

confidence: 99%