Dataset Information

Survey on natural language processing in medical image analysis.

ABSTRACT: Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.

SUBMITTER: Liu Z

PROVIDER: S-EPMC10950114 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Survey on natural language processing in medical image analysis.

Liu Zhengliang Z He Mengshen M Jiang Zuowei Z Wu Zihao Z Dai Haixing H Zhang Lian L Luo Siyi S Han Tianle T Li Xiang X Jiang Xi X Zhu Dajiang D Cai Xiaoyan X Ge Bao B Liu Wei W Liu Jun J Shen Dinggang D Liu Tianming T

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences 20220801 8

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the cur ...[more]

PMID: 36097765

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Project description:BackgroundCerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, including emergency operation. Computed tomography (CT) is essential for identifying cerebral hemorrhage, but its effectiveness is limited by the availability of experienced radiologists, especially in resource-constrained regions or when shorthanded during holidays or at night. Despite advancements in artificial intelligence-driven diagnostic tools, most require technical expertise. This poses a challenge for widespread adoption in radiological imaging. The introduction of advanced natural language processing (NLP) models such as GPT-4, which can annotate and analyze images without extensive algorithmic training, offers a potential solution.ObjectiveThis study investigates GPT-4's capability to identify and annotate cerebral hemorrhages in cranial CT scans. It represents a novel application of NLP models in radiological imaging.MethodsIn this retrospective analysis, we collected 208 CT scans with 6 types of cerebral hemorrhages at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, between January and September 2023. All CT images were mixed together and sequentially numbered, so each CT image had its own corresponding number. A random sequence from 1 to 208 was generated, and all CT images were inputted into GPT-4 for analysis in the order of the random sequence. The outputs were subsequently examined using Photoshop and evaluated by experienced radiologists on a 4-point scale to assess identification completeness, accuracy, and success.ResultsThe overall identification completeness percentage for the 6 types of cerebral hemorrhages was 72.6% (SD 18.6%). Specifically, GPT-4 achieved higher identification completeness in epidural and intraparenchymal hemorrhages (89.0%, SD 19.1% and 86.9%, SD 17.7%, respectively), yet its identification completeness percentage in chronic subdural hemorrhages was very low (37.3%, SD 37.5%). The misidentification percentages for complex hemorrhages (54.0%, SD 28.0%), epidural hemorrhages (50.2%, SD 22.7%), and subarachnoid hemorrhages (50.5%, SD 29.2%) were relatively high, whereas they were relatively low for acute subdural hemorrhages (32.6%, SD 26.3%), chronic subdural hemorrhages (40.3%, SD 27.2%), and intraparenchymal hemorrhages (26.2%, SD 23.8%). The identification completeness percentages in both massive and minor bleeding showed no significant difference (P=.06). However, the misidentification percentage in recognizing massive bleeding was significantly lower than that for minor bleeding (P=.04). The identification completeness percentages and misidentification percentages for cerebral hemorrhages at different locations showed no significant differences (all P>.05). Lastly, radiologists showed relative acceptance regarding identification completeness (3.60, SD 0.54), accuracy (3.30, SD 0.65), and success (3.38, SD 0.64).ConclusionsGPT-4, a standout among NLP models, exhibits both promising capabilities and certain limitations in the realm of radiological imaging, particularly when it comes to identifying cerebral hemorrhages in CT scans. This opens up new directions and insights for the future development of NLP models in radiology.Trial registrationClinicalTrials.gov NCT06230419; https://clinicaltrials.gov/study/NCT06230419.

Project description:To test the feasibility of using text mining to depict meaningfully the experience of pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to propose methods for longitudinal visualization of pain status.Text from 4409 clinical encounters for 33 men enrolled in a 15-year longitudinal clinical/molecular autopsy study of metastatic prostate cancer (Project to ELIminate lethal CANcer) was subjected to natural language processing (NLP) using Unified Medical Language System-based terms. A four-tiered pain scale was developed, and logistic regression analysis identified factors that correlated with experience of severe pain during each month.NLP identified 6387 pain and 13 827 drug mentions in the text. Graphical displays revealed the pain 'landscape' described in the textual records and confirmed dramatically increasing levels of pain in the last years of life in all but two patients, all of whom died from metastatic cancer. Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002). Surprisingly, no severe or controlled pain was detected in two of 33 subjects' clinical records. Additionally, the NLP algorithm proved generalizable in an evaluation using a separate data source (889 Informatics for Integrating Biology and the Bedside (i2b2) discharge summaries).Patterns in the pain experience, undetectable without the use of NLP to mine the longitudinal clinical record, were consistent with clinical expectations, suggesting that meaningful NLP-based pain status monitoring is feasible. Findings in this initial cohort suggest that 'outlier' pain phenotypes useful for probing the molecular basis of cancer pain may exist.The results are limited by a small cohort size and use of proprietary NLP software.We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records. These methods may be useful for monitoring pain management and identifying novel cancer phenotypes.

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Survey on natural language processing in medical image analysis.

Publications

Survey on natural language processing in medical image analysis.

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