The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It

• URL: http://arxiv.org/abs/2406.13181v1
• Date: Wed, 19 Jun 2024 03:25:31 GMT
• Title: The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It
• Authors: Aaron Nicolson, Shengyao Zhuang, Jason Dowling, Bevan Koopman,
• Abstract summary: This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we incorporate detailed patient information such as a vital signsperiodic, medications, and clinical history to enhance diagnostic accuracy.
• Score: 12.61239008314719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on CXR images and limited radiology data, overlooking valuable information from patient health records, particularly from emergency departments. Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we incorporate detailed patient information such as aperiodic vital signs, medications, and clinical history to enhance diagnostic accuracy. We introduce a novel approach to transform these heterogeneous data sources into embeddings that prompt a multimodal language model, significantly enhancing the diagnostic accuracy of generated radiology reports. Our comprehensive evaluation demonstrates the benefits of using a broader set of patient data, underscoring the potential for enhanced diagnostic capabilities and better patient outcomes through the integration of multimodal data in CXR report generation.

Related papers

• Resource-Efficient Medical Report Generation using Large Language Models [3.2627279988912194]
  Medical report generation is the task of automatically writing radiology reports for chest X-ray images. We propose a new framework leveraging vision-enabled Large Language Models (LLM) for the task of medical report generation.
  arXiv  Detail & Related papers  (2024-10-21T05:08:18Z)
• 3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models [51.855377054763345]
  This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model for generating radiology reports from 3D CT scans. Experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality.
  arXiv  Detail & Related papers  (2024-09-28T12:31:07Z)
• D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions [8.50767187405446]
  We propose D-Rax -- a domain-specific, conversational, radiologic assistance tool. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations.
  arXiv  Detail & Related papers  (2024-07-02T18:43:10Z)
• Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning [16.849933628738277]
  Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. This paper introduces a novel RRG method, textbfLM-RRG, that integrates large models (LMs) with clinical quality reinforcement learning. Experiments on the MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.
  arXiv  Detail & Related papers  (2024-03-11T13:47:11Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.