Radiology Reports Research Articles

Text mining approach for feature extraction and cartilage disease grade classification using knee MRI radiology reports

MRI radiology reporting processes can be improved by exploiting structured and semantically labelled data that can be fed to artificial intelligence (AI) tools. AI-based tools assisting radiology reporting can help to automatically individuate cartilage grading in textual magnetic resonance imaging (MRI) reports, thus supporting clinicians' decisions regarding medical imaging utilisation, diagnosis and treatment. In this study, we extracted information (clinical findings, observations, anatomical regions, etc.) and classified knee cartilage degradation from medical reports utilising transfer-learning techniques applied to the Bidirectional Encoder Representations from Transformers (BERT) model and its variants, pre-trained on an Italian-language corpus. To realise this objective, we used a dataset of 750 MRI knee reports written by three radiologists who contributed to a manual annotation process to perform text classification (TC) and named entity recognition (NER) tasks. The dataset was obtained from an internal database of the IRCCS SYNLAB SDN. Seventy percent of the dataset was used for training, 10% was used for validation and 20% was used for testing. The best-performing configurations for NER and TC tasks were based on the pre-trained BERT model. The macro F1-scores obtained with the NER and TC models are 0.89 and 0.81, respectively. The accuracies calculated on the test set for both tasks are 0.96 and 0.99, respectively.

7949 Unraveling a Unique Presentation of Paget’s Disease of Bone

Abstract Disclosure: S. Puri: None. L. Bovee: None. R. Narla: None. Background: Paget's Disease of Bone (PDB) is a disorder characterized by rapid bone destruction and repair, which results in distortion of bone architecture. PDB occurs most commonly in adults over 55 and of European descent. PDB presents with both lytic and sclerotic lesions, predominantly in the axial skeleton. Serum alkaline phosphatase (ALP) levels and bone turnover markers (BTMs) are typically elevated in PDB. Clinical Case: A 53-year-old African-American woman with a history of breast cancer, PTSD, depression, and obesity (BMI 37) presented with worsening back pain over the past six months. Her pain would start at the center of her back and radiate to her left lower extremity. She endorsed rapid weight loss and night sweats and had lumbar bony tenderness on exam, prompting a spinal MRI. Spinal MRI was significant for diffuse sclerosis of the L3 vertebral body only. The radiology report provided a broad differential for this finding, including metastases, lymphoma, infection, PDB, and hemangioma. Due to progression of the patient’s back pain and concern for possible malignancy, a whole-body SPECT was completed which showed isolated tracer uptake into the L3 vertebral body extending into the pedicles and spinous process. Similar findings were noted on bone scans dating back to 2001. Per the radiology report, the presence of sclerosis with minimal expansion is most suggestive of PDB. Endocrinology was consulted for guidance on treatment and surveillance imaging for this patient. The endocrinology team discussed the case with radiology, who confirmed that the patient’s imaging findings had classic features of PDB. Bone specific ALP (B-ALP) and procollagen type 1 amino-terminal propeptide (P1NP) as bone turnover markers (BTMs) were within normal limits. A DEXA scan is pending to evaluate for concurrent osteoporosis. Clinical Lessons: PDB was favored as the most likely diagnosis primarily due to the sclerotic appearance of the patient’s vertebral lesion on imaging, radicular symptoms, and exam finding of bony point tenderness. Other diagnoses, such as metastatic disease, were considered and were thought to be unlikely given that the vertebral lesion is monostotic and stable. This case highlights the necessity of carefully evaluating radiographic findings and clinical history in the diagnosis of PDB. In this case, the patient was from a demographic group unlikely to have PDB and, unexpectedly, did not have elevated BTMs. It is important to consider PDB on the differential diagnosis in cases such as this because PDB can present in myriad ways and in a variety of patient populations. Presentation: 6/2/2024

Uterine Sarcoma or Degenerating Fibroid? Validating the New Consensus Magnetic Resonance Imaging Algorithm for Evaluating Atypical Uterine Masses.

The aim of the study is to assess the validity of a recently published consensus magnetic resonance imaging (MRI) diagnostic algorithm for differentiating degenerating leiomyomas from uterine sarcomas and other atypical appearing uterine malignancies. Atypical uterine masses on pelvic MRI were identified using a radiology report search engine and teaching files with the keywords "atypical leiomyoma," "atypical fibroid," and "sarcoma." All cases were pathology-proven. Two radiologists blinded to clinical, surgical, and pathologic reports retrospectively and independently reviewed 40 pelvic MRI examinations dated 1/2007-9/2022 to determine whether the masses appeared benign or malignant, using the 2022 consensus atypical uterine mass flow chart. Imaging features assessed included intermediate/high signal intensity (SI) at T2-weighted imaging, high diffusion weighted imaging SI (equal or higher SI than endometrium or lymph nodes on high b value imaging), apparent diffusion coefficient (ADC) value ≤0.905 × 10-3 mm2/s, peritoneal metastases, and abnormal lymph nodes. Among the 40 atypical uterine mass cases reviewed, 24 masses were benign (22 leiomyomas, 1 adenomyoma, and 1 borderline ovarian tumor) and 16 masses were malignant (6 leiomyosarcomas, 6 carcinosarcomas, 2 endometrial stromal sarcomas, 1 high-grade adenosarcoma, and 1 low-grade uterine sarcoma). Sensitivity, specificity, positive predictive value, and negative predictive value of whether a mass was benign or malignant were 75%, 95.8%, 92.3%, and 85% for reader 1, and 81.2%, 91.7%, 86.7%, and 88% for reader 2, respectively. Interrater agreement was strong, with a kappa statistic of 0.89. When excluding nonleiomyosarcoma uterine malignancies, sensitivity and negative predictive value improved to 100%. The new consensus pelvic MRI algorithm for evaluating atypical uterine masses has good specificity, sensitivity, positive predictive value, and negative predictive value for determining malignancy, particularly for uterine sarcomas that are leiomyosarcomas. However, if ADC value is near but not below 0.905 × 10-3 mm2/s, the mass may still be malignant, especially if a b value lower than 1000 is used. If the atypical uterine mass is predominantly endometrial, morphological features on T2 and postgadolinium sequences should guide suspicion, as some atypical appearing nonleiomyosarcoma uterine malignancies may have an ADC value greater than 0.905 × 10-3 mm2/s.

Incidental findings on MRI for the evaluation of endometriosis: prevalence and clinical significance.

This study aimed to analyze the prevalence and clinical significance of incidental findings on MRI for endometriosis. Differences between patients with and without evidence of deep infiltrating endometriosis on MRI were to be examined. This was a retrospective, descriptive cross-sectional single-center study. All patients who received a pelvic MRI for endometriosis between April 2021 and February 2023 were included. The presence and frequency of incidental findings were noted after review of all MR images and radiology reports. The potential clinical significance of the findings was analyzed. Differences in the frequency of incidental findings between patients with and without evidence of deep infiltrating endometriosis on MRI were evaluated, utilizing the Chi-square test, Fisher's exact test and Mann-Whitney U-test. 303 consecutive patients (mean age, 33.4 years ± 8.3) were evaluated. Incidental findings were noted in 299/303 (98.7%) patients. Most frequently, ossification of the hip acetabular rim and degenerative changes of the lumbar spine were noted. In 25/303 (8.3%) patients, incidental findings had high clinical significance. For specific incidental findings, significantly higher prevalences were found in patients with than in patients without evidence of deep infiltrating endometriosis on MRI (hip acetabular rim ossification, p = 0.041; annulus fibrosus fissures, p = 0.006; gallstones, p = 0.042). Incidental findings are very common on pelvic MRI for endometriosis. The detection of incidental findings can lead to the diagnosis of relevant diseases and thus enable early treatment. On the other hand, many incidental findings have no, only minor, or uncertain consequences.

Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing.

Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision-making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which have been labeled automatically (self-supervised mode) and tend to overfit. In this study, we investigate the quality of uncertainty estimates from a range of current state-of-the-art predictive models applied to the problem of observation detection in radiology reports. This problem remains understudied for Natural Language Processing in the healthcare domain. We demonstrate that Gaussian Processes (GPs) provide superior performance in quantifying the risks of three uncertainty labels based on the negative log predictive probability (NLPP) evaluation metric and mean maximum predicted confidence levels (MMPCL), whilst retaining strong predictive performance. Our conclusions highlight the utility of probabilistic models applied to "noisy" labels and that similar methods could provide utility for Natural Language Processing (NLP) based automated labeling tasks.

Streamlining Radiology Reporting: A hands-free approach with voice-to-text and generative AI

ObjectiveIn radiology, report generation is critical for patient management, though can be time-consuming and labor-intensive. This study aims to investigate the feasibility of a hands-free radiology reporting system, leveraging voice-to-text technology and generative AI to improve time efficiency, report standardization, and reduce radiologist workload. Methods100 chest and musculoskeletal radiographs reports were generated using both traditional methods and an automated process using generative AI. To use generative AI, a radiologist dictated their findings into a voice-to-text software (Dragon), creating a raw text transcript. GPT-4 was then used to develop a report based on our institution’s template. Completion time for each traditional report and voice-to-text transcript was recorded. All reports were then evaluated for comprehensiveness, clarity, factual accuracy, and conciseness on a 5-point Likert scale. ResultsThe AI-driven system demonstrated considerable proficiency in transcribing and generating radiology reports, albeit with occasional inaccuracies. While it achieved a high level of precision in standard scenarios, its performance varied in more complex cases. Notably, the system significantly reduced the time taken to generate reports by 38% (p < 0.0001), signifying a potential increase in overall radiological workflow efficiency. ConclusionThis study highlights the potential of voice-to-text and generative AI integration in radiology reporting. Increased time efficiency, even in standard scenarios, can allow radiologists to focus more on complex cases and patient care. However, further development and refinement are required to address comprehensiveness and occasional inaccuracies. Ongoing advancements in AI and speech recognition technologies suggest a positive outlook for future implementations in medical reporting.

DACG: Dual Attention and Context Guidance model for radiology report generation

Medical images are an essential basis for radiologists to write radiology reports and greatly help subsequent clinical treatment. The task of generating automatic radiology reports aims to alleviate the burden of clinical doctors writing reports and has received increasing attention this year, becoming an important research hotspot. However, there are severe issues of visual and textual data bias and long text generation in the medical field. Firstly, Abnormal areas in radiological images only account for a small portion, and most radiological reports only involve descriptions of normal findings. Secondly, there are still significant challenges in generating longer and more accurate descriptive texts for radiology report generation tasks. In this paper, we propose a new Dual Attention and Context Guidance (DACG) model to alleviate visual and textual data bias and promote the generation of long texts. We use a Dual Attention Module, including a Position Attention Block and a Channel Attention Block, to extract finer position and channel features from medical images, enhancing the image feature extraction ability of the encoder. We use the Context Guidance Module to integrate contextual information into the decoder and supervise the generation of long texts. The experimental results show that our proposed model achieves state-of-the-art performance on the most commonly used IU X-ray and MIMIC-CXR datasets. Further analysis also proves that our model can improve reporting through more accurate anomaly detection and more detailed descriptions. The source code is available at https://github.com/LangWY/DACG.

Enhancing Aortic Aneurysm Surveillance: Transformer NLP for Flagging and Measuring in Radiology Reports

Incidental findings of aortic aneurysms (AAs) often go unreported, and established patients are frequently lost to follow-up. Natural language processing (NLP) offers a promising solution to address these issues. While rule-based NLP methods have shown some success, recent advancements in transformer-based large language models (LLMs) remain underutilized. This study has three aims: (1) to evaluate the effectiveness of our innovative transformer-based NLP pipeline regarding AA detection; (2) to detail the clinical impact by quantifying the number of patients who could benefit from such technology; and (3) to use this information to help coordinate appointments with patients, ensuring proper monitoring and management. 3229 radiology reports were divided into three batches with varying class balance. Each entry was processed through our innovative NLP pipeline, where it was fragmented using regular expression (regex) functions to isolate relevant textual segments. These segments were subsequently processed through our "question and find" (Q&F) function, powered by Google's BERT, a well-established transformer LLM. This Q&F function extracted aortic diameter measurements, flagging measurements that exceeded a predefined threshold. Following detection, we conducted comprehensive chart reviews and contacted primary care providers (PCPs) and patients to categorize aneurysms as "known" or "incidental." We also assessed whether patients with known aneurysms were adhering to regular yearly screenings and coordinated follow-up appointments. Evaluation of the three batches showed high F1 scores: 99.4% (95% CI [98.5-100]), 96.7% (95% CI [95.0-98.2]), and 98.9% (95% CI [98.0-99.6]). Overall measurement accuracy was 98.9% (95% CI [97.6-100]), 99.6% (95% CI [99.3-99.9]), and 98.1% (95% CI [96.8-99.4]). Compared to manual chart reviews, the NLP system demonstrated superior accuracy and fewer errors: 12 vs. 22 (p=0.084), 47 vs. 98 (p=0.000021), and 31 vs. 53 (p=0.015). Of the 412 patients investigated, 58 (14.1%) involved incidental findings, 54 patients (15.3%) were lost to follow-up, 39 patients (55.7%) were successfully contacted, and 37 follow-up appointments (12.1%) were successfully coordinated. The high-performance metrics from our study demonstrate that transformer-based NLP can enhance aortic aneurysm surveillance. Our subsequent comprehensive patient profiling highlighted the need for such a system as a safety net within the electronic medical record (EMR), systematically reviewing radiology reports to detect incidental findings and patients lost to follow-up. This ensures appropriate referrals and monitoring, improving patient outcomes and healthcare efficiency through timely clinical interventions.

Analysis of ChatGPT in the Triage of Common Spinal Complaints

ChatGPT is a natural language processing chatbot with a significant prevalence in modern media with a clear application in the medical triage workflow. ChatGPT has shown significant capacity for understanding clinical vignettes, radiology reports, and even passing the American Board of Neurological Surgery board examination. There has never been an evaluation of the chatbot in triage and diagnosing spinal vignettes common to primary and urgent care practice. Fifteen clinical scenarios were created to mimic spinal complaints common to primary and urgent care scenarios. GPT-4 was instructed to assess the situation as if it was in a primary care office and determine diagnosis, imaging recommendations, and if emergency room (ER) or operative referral was necessary. Answers were recorded and the results compared to those of attending and resident respondents. GPT-4 provided the most likely diagnosis in each scenario. Additionally, it recommended reasonable clinical management of each scenario which would fall within standard practice guidelines. ChatGPT tended toward over-referral to the ER; however, this was not significant. GPT-4 was noninferior in all categories when compared to respondents. ChatGPT is a powerful tool for primary triage of spinal issues. It can rapidly and accurately evaluate clinical scenarios and provide clear diagnostic reasoning. GPT-4 is not designed for medical use and will provide a disclaimer as such. It did tend toward over-referring patients to the ER. With specific training, it is likely that artificial intelligence and natural language processing chatbots will become widely used in primary triage of spinal issues.

Comparing Commercial and Open-Source Large Language Models for Labeling Chest Radiograph Reports.

Background Rapid advances in large language models (LLMs) have led to the development of numerous commercial and open-source models. While recent publications have explored OpenAI's GPT-4 to extract information of interest from radiology reports, there has not been a real-world comparison of GPT-4 to leading open-source models. Purpose To compare different leading open-source LLMs to GPT-4 on the task of extracting relevant findings from chest radiograph reports. Materials and Methods Two independent datasets of free-text radiology reports from chest radiograph examinations were used in this retrospective study performed between February 2, 2024, and February 14, 2024. The first dataset consisted of reports from the ImaGenome dataset, providing reference standard annotations from the MIMIC-CXR database acquired between 2011 and 2016. The second dataset consisted of randomly selected reports created at the Massachusetts General Hospital between July 2019 and July 2021. In both datasets, the commercial models GPT-3.5 Turbo and GPT-4 were compared with open-source models that included Mistral-7B and Mixtral-8 × 7B (Mistral AI), Llama 2-13B and Llama 2-70B (Meta), and Qwen1.5-72B (Alibaba Group), as well as CheXbert and CheXpert-labeler (Stanford ML Group), in their ability to accurately label the presence of multiple findings in radiograph text reports using zero-shot and few-shot prompting. The McNemar test was used to compare F1 scores between models. Results On the ImaGenome dataset (n = 450), the open-source model with the highest score, Llama 2-70B, achieved micro F1 scores of 0.97 and 0.97 for zero-shot and few-shot prompting, respectively, compared with the GPT-4 F1 scores of 0.98 and 0.98 (P > .99 and < .001 for superiority of GPT-4). On the institutional dataset (n = 500), the open-source model with the highest score, an ensemble model, achieved micro F1 scores of 0.96 and 0.97 for zero-shot and few-shot prompting, respectively, compared with the GPT-4 F1 scores of 0.98 and 0.97 (P < .001 and > .99 for superiority of GPT-4). Conclusion Although GPT-4 was superior to open-source models in zero-shot report labeling, few-shot prompting with a small number of example reports closely matched the performance of GPT-4. The benefit of few-shot prompting varied across datasets and models. © RSNA, 2024 Supplemental material is available for this article.

Leveraging artificial intelligence for timely detection and referral of incidental pulmonary nodules at a safety-net hospital.

422 Background: Incidental pulmonary nodules (IPNs) are commonly reported through a multitude of radiologic exams. A small number of these IPNs can harbor lung cancer (LC). To aide LC detection rates, hospitals must identify and validate the completion of appropriate follow-up regarding IPNs in a timely manner. The problem with timely referral and completion of work up is found to be amplified in safety-net settings with limited resources. John Peter Smith (JPS) hospital, a safety-net hospital, has developed a method to identify, track, and refer patients presenting with IPNs. Methods: JPS has created an algorithm to classify all IPNs by means of Thynk Health, an Artificial Intelligence (AI) platform that uses Natural Language Processing (NLP) to identify and track incidental findings and lung cancer screening patients (LCS). The algorithm created a scoring system by combining Fleischner Society (2017) and the American College of Radiology (ACR) LCS (Lung-RADS) guideline recommendations. The scoring system was designed to mimic Lung-RADS established by the ACR (1, 2, 3, 4A, and 4B). The assigned score creates a risk stratification to assist providers in the patient’s care plan. Using NLP, the project queried radiologic exams performed at JPS searching for key terminology relating to IPNs. Findings were then reviewed by radiology staff for validity and assigned a score based on the algorithm. Program exclusions, when identified in the radiology report, included: patient &lt; 35 years, patient has known cancer or under surveillance of oncology team, abnormalities favoring infectious process, LCS patients and when nodule size is not reported in the radiologist’s dictation. Results: A current state analysis was completed from 3/25/24 to 4/25/24. The AI platform analyzed 5,276 CT exams (2049 abdomen, 1347 head/neck, and 1880 chest) involving partial or complete imaging of the lungs. A total of 727 (13.8%) IPNs were flagged for review. After excluding exams falling outside of the set program parameters, a total of 178 (3.4%) IPNs were identified. 21 (11.8%) of the IPNs identified received a score of 4A or 4B. Patients found to have a score of 4A (11, 6.2%) and 4B (10, 5.6%) underwent additional review. Conclusions: Our team has created a unique system that can be used as a model for other large health systems. By leveraging technology, we are able to successfully identify key factors associated with IPNs to help ensure patients receive the recommended care in a timely manner. Results of JPS incidental pulmonary nodule program: first monthly analysis. JPS Pulmonary Nodule Score 4A 4B Number of Patients (% of IPNs) 11 (6.2%) 10 (5.6%) Median Age (years) 70 68.5 JPS Assigned PCP 1 7 (63.6%) 7 (70%) Median Smoking History (PY) 2 6.4 24.5 Average Nodule Size (mm) 11.5 34.3 Pulmonary Referral 3/11 (27.3%) 7/10 (70%) Positive for Cancer 0 3/4 (75%) 1 Primary care provider. 2 Pack years.

Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports.

Background Natural language processing (NLP) is commonly used to annotate radiology datasets for training deep learning (DL) models. However, the accuracy and potential biases of these NLP methods have not been thoroughly investigated, particularly across different demographic groups. Purpose To evaluate the accuracy and demographic bias of four NLP radiology report labeling tools on two chest radiograph datasets. Materials and Methods This retrospective study, performed between April 2022 and April 2024, evaluated chest radiograph report labeling using four NLP tools (CheXpert [rule-based], RadReportAnnotator [RRA; DL-based], OpenAI's GPT-4 [DL-based], cTAKES [hybrid]) on a subset of the Medical Information Mart for Intensive Care (MIMIC) chest radiograph dataset balanced for representation of age, sex, and race and ethnicity (n = 692) and the entire Indiana University (IU) chest radiograph dataset (n = 3665). Three board-certified radiologists annotated the chest radiograph reports for 14 thoracic disease labels. NLP tool performance was evaluated using several metrics, including accuracy and error rate. Bias was evaluated by comparing performance between demographic subgroups using the Pearson χ2 test. Results The IU dataset included 3665 patients (mean age, 49.7 years ± 17 [SD]; 1963 female), while the MIMIC dataset included 692 patients (mean age, 54.1 years ± 23.1; 357 female). All four NLP tools demonstrated high accuracy across findings in the IU and MIMIC datasets, as follows: CheXpert (92.6% [47 516 of 51 310], 90.2% [8742 of 9688]), RRA (82.9% [19 746 of 23 829], 92.2% [2870 of 3114]), GPT-4 (94.3% [45 586 of 48 342], 91.6% [6721 of 7336]), and cTAKES (84.7% [43 436 of 51 310], 88.7% [8597 of 9688]). RRA and cTAKES had higher accuracy (P < .001) on the MIMIC dataset, while CheXpert and GPT-4 had higher accuracy on the IU dataset. Differences (P < .001) in error rates were observed across age groups for all NLP tools except RRA on the MIMIC dataset, with the highest error rates for CheXpert, RRA, and cTAKES in patients older than 80 years (mean, 15.8% ± 5.0) and the highest error rate for GPT-4 in patients 60-80 years of age (8.3%). Conclusion Although commonly used NLP tools for chest radiograph report annotation are accurate when evaluating reports in aggregate, demographic subanalyses showed significant bias, with poorer performance in older patients. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Cai in this issue.

Impact of applying machine learning to the electronic medical record on prediction of cancer-associated thrombosis.

409 Background: Cancer-associated thrombosis (CAT) is preventable among high-risk individuals with prophylactic anticoagulation. Risk assessment tools focus only on the initial period after cancer diagnosis; however, some patients may face different risks during different periods of their cancer journey. We developed a longitudinal machine learning system to predict the risk of CAT throughout cancer treatment. Methods: Using electronic health record data at Princess Margaret Cancer Centre, we assembled a cohort of people with thoracic and gastrointestinal cancer receiving systemic treatments between August 1, 2017 and December 31, 2019. We manually labelled 5,000 CT scans and 500 Doppler ultrasounds for the occurrence of CAT. We fine-tuned open-source large language models (LLMs) on those labelled reports, which we then used to automatically detect CAT among all 37,000 CT scans and Dopplers. Finally, we trained longitudinal machine learning systems to predict CAT within 90 days after each cancer treatment. LLMs and the CAT risk prediction system were evaluated among the held-out test cohort of people whose first treatment occurred in 2019. Results: The overall cohort included 2,031 patients and 21,375 treatment sessions. When classifying radiology reports, the fine-tuned LLM achieved an area under the receiver operating characteristic curve (AUROC) of 0.758 for DVT and 0.988 for PE in the test cohort. CAT occurred within 90 days after 6.23% of treatment sessions. The best ML system predicted the risk of CAT within 90 days with an AUROC of 0.651 (95% CI, 0.620-0.678). Considering only the first treatment per patient, treatments within the first 90 days after the first treatment, and treatments after 90 days, the system achieved AUROC of 0.639 (95% CI, 0.568-0.704), 0.599 (95% CI, 0.559-0.642), and 0.741 (95% CI, 0.699-0.783), respectively. Conclusions: Machine learning can longitudinally predict CAT among patients receiving systemic therapy for aerodigestive cancers. These systems could personalize prophylactic anticoagulation by identifying specific periods when patients face sufficient risk to warrant prevention.

S213 A Systematic Approach to Pancreatic Cysts: An Investigation of the Impact of Standardized Radiology Report Templates on the Management of New Pancreatic Cysts

S213 A Systematic Approach to Pancreatic Cysts: An Investigation of the Impact of Standardized Radiology Report Templates on the Management of New Pancreatic Cysts

Artificial Intelligence in CT Diagnosis: Current Status and Future Prospects for Ear Diseases

The human ear, with its complex structures like the ossicular chain, cochlea, and auditory nerve, plays a crucial role in hearing and balance. Common ear diseases, such as hearing loss, tinnitus, facial paralysis and vertigo, affect the quality of life of millions in China. Computed tomography (CT) has seen significant advancements since its introduction to China in 2000. The resolution improves from millimeter to sub-millimeter levels, and further, to 10 μm through bone-dedicated CT technology. The advancements have made CT the preferred method for diagnosing various ear conditions, including congenital malformations, trauma, inflammation, and neoplasm. Artificial intelligence (AI) has brought significant breakthroughs in CT diagnosis. Automatic segmentation of ear structures has dramatically improved with the advent of ultra-high-resolution computed tomography (U-HRCT). AI-driven measurement tools are enhancing the precision and personalization of surgical planning, while deep learning-based anomaly detection is utilized to address the challenges of detecting diverse ear lesions. Furthermore, AI-driven natural language processing and large language models are revolutionizing the generation of radiology reports, providing accurate and standardized diagnostic information. Despite ongoing challenges, the integration of AI into CT is expected to advance the otological field, leading to more precise and personalized treatment for ear diseases.

Influence of Prior Imaging Review on Recommendations for Additional Diagnostic Testing: Retrospective Analysis of Imaging Reports in a Chiropractic Radiology Practice

ObjectiveThe purpose of this study was to assess the influence of prior imaging review on recommendations for additional diagnostic testing in an academic chiropractic radiology practice. A secondary aim was to explore the influence of prior imaging review on radiographic interpretation. MethodsWe retrospectively reviewed radiology reports generated from July 18, 2022, to July 18, 2023, from the Palmer College of Chiropractic main campus (Davenport, Iowa) clinic system. Imaging interpretation included an automated search for prior images in an internal picture archival and communication system (PACS). Images from regional health system databases were available and sought by radiologists when (1) unclear radiologic findings had potential clinical implications or (2) prior imaging could clarify potential problems detected in a clinical history. Data were abstracted to a secure adaptive electronic questionnaire and analyzed descriptively. ResultsWe reviewed 1712 radiographic and 165 musculoskeletal diagnostic ultrasound reports for 1552 unique individuals (811 [52.3%] females and 741 [47.7%] males) with a mean age of 42.1 years (range, 2-93 years). Prior imaging was described in 417 (22.2%) reports; 246 (58.9%) indicated images from internal PACS, 192 (46.0%) indicated images from external PACS, and 21 noted both internal and external PACS. Prior imaging findings were credited with answering a clinical question in 98 (23.5%), and a radiographic question in 228 (54.7%) of 417 reports. The process negated the need for follow-up diagnostic testing recommendations in 119 (28.5%) instances, leading to additional imaging recommendations in 19 (4.6%). ConclusionData obtained in this study suggest that comparing current and previous imaging may help reduce unnecessary additional imaging or follow-up diagnostic testing recommendations. Prior imaging review may also facilitate diagnostic confidence and interpretation clarity.

ASYMMETRICAL CORTICAL VEIN SIGN (ACVS) PREDICT NEUROLOGICAL AND RADIOLOGICAL IMPROVEMENT IN LARGE AND DISTAL VESSEL OCCLUSION POST REPERFUSION THERAPY

ABSTRACT Purpose To investigate the dynamic changes in ACVS on SWIs in patients who underwent reperfusion therapy and evaluate their association with neurological and radiological improvement Materials and Methods All patients treated for acute ischemic stroke between June 2020 and June 2022, in a single tertiary centre (Hospital Sultan Abdul Aziz Shah) who underwent reperfusion therapy and MRI imaging were retrospectively identified based on clinical and radiology reports. Expected Results This study hypothesizes an improvement in ACVS is expected to correlate with favourable neurological outcomes (MRS at 3 months and NIHSS score) and radiological outcome (DWI / ASPECT scores and Modified Mori score). Conversely, a worsening of ACVS is anticipated to be associated with unfavourable neurological and radiological outcomes. Conclusion Asymmetrical Cortical Vein Signs (ACVS) can serve as a valuable predictor of neurological and radiological outcomes in patients with large and distal vessel occlusion after reperfusion therapy Keywords: ACVS (The Asymmetrical cortical vein sign), Ischemic penumbra , Large vessel occlusion (LVO), Distal vessel occlusion (DVO)

The Accuracy of Radiographers Preliminary Image Evaluations of Axial Radiographs: a Prospective Longitudinal Study at Al-Hussein Hospital, Iraq

Introduction: Modern emergency departments are under increasing pressure that can only be alleviated by new approaches to meeting demand. One strategy could be the adoption of radiographer preliminary image evaluations (PIEs), which would significantly reduce the time it takes emergency teams to receive critical imagery results. The aim of this study was to examine the accuracy of radiographers’ interpretations of axial radiographs and the impact a short training program had on their image interpretation skills. Methods: Forty-one radiographers volunteered to participate in the study. From 15 January to 15 April 2023 radiographers produced PIEs for 24 axial radiographs which were compared to the radiology reports for the same images. The accuracy each of the radiographers’ PIEs were categorized as true positive, true negative, false positive and false negative, with numerical scores to enable mean accuracies to be calculated. The radiographers’ accuracy was then reassessed on the same images after they had completed a two-week training program on axial skeletal radiograph interpretation. Results: The study reported that demonstrated that most (TP) radiographer PIEs (M = 5.66, SD = 1.24) were about facial bone fracture. Moreover, this study demonstrated that most (FN/TP) radiographer PIEs (M = 3.83, SD = 1.69) were that cases about inflammatory disease of the axial spine, such as ankylosing spondylitis. In contrast, the most common cases misinterpreted were about cervical spine disorders such as diffuse idiopathic skeletal hyperostosis (DISH) (M = 2.49, SD = 1.36). Years of experience in radiography showed a positive but not statistically significant influence on accuracy. Additionally, post training mean accuracy significantly improved. Conclusion: This study found radiographers provided quite accurate PIEs for facial bone fractures when compared to the full radiology reports of the same images. The findings also highlighted specific pathologies that were frequently misinterpreted. A two-week training program was found to be effective at improving radiographers’ ability to correctly identify and describe abnormalities present in axial radiographs.

Integrated model of secondary fracture prevention in primary care (INTERCEPT): protocol for a cluster randomised controlled multicentre trial

BackgroundOsteoporotic fractures signal severely compromised bone strength and are associated with a greatly increased risk of refracture. Despite the availability of effective and safe medications that reduce fracture risk, 70–80% of patients are inadequately investigated or treated for osteoporosis following an initial fracture, constituting a significant ‘osteoporosis care gap’. Optimal methods of bridging this gap with primary care at the forefront of secondary fracture prevention remain undetermined. This protocol describes a cluster randomised controlled trial to evaluate the effectiveness of a novel integrated model of secondary fracture prevention and management in primary care.MethodsThe cluster randomised controlled trial involves multiple branches of a community-based radiology provider (CRP), a hospital-based secondary fracture prevention program (SFPP) and numerous primary care practices in metropolitan Sydney that refer to either the CRP or SFPP. Using natural language processing tools, patients diagnosed with a potential osteoporotic fracture will be identified by automatically screening radiology reports generated at the CRP or SFPP. The primary care practices that these patients attend will be randomised (1:1) to either the intervention or usual care. The intervention consists of (i) electronic and fax alerts informing the practice/primary care physician that their patient has been diagnosed with a potential osteoporotic fracture; (ii) provision of osteoporosis management guidelines and (iii) follow-up surveys at 4 weeks and 6 months. Practices in the usual care (control) group will receive no alerts and provide usual care. The primary outcome is the proportion of patients undergoing a bone density scan and/or filling a prescription for osteo-protective pharmacotherapy within 3 months of the initial diagnostic imaging report. Secondary outcomes are the proportion of patients: (i) undergoing an osteoporosis-related blood test within 3 months of the initial diagnostic imaging report; (ii) initiated on a chronic disease management plan within 3 months of the diagnostic report, and (iii) filling a second prescription for osteo-protective pharmacotherapy within 9 months post initial diagnostic imaging report. Outcomes will be obtained through de-identified linked data from Medical Benefits Schedule and Pharmaceutical Benefits Scheme held by the Australian Institute of Health and Welfare.DiscussionThis is the first randomised trial to integrate case-detection of potential osteoporotic fractures in a hospital and community setting with direct alerts to the patient’s primary care provider. This study will determine whether such an intervention is effective in improving investigation and/or treatment rates of osteoporosis in patients with a potential osteoporotic fracture.Trial registrationThis study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12623000658617p.

An open-source fine-tuned large language model for radiological impression generation: a multi-reader performance study

BackgroundThe impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remainder of a radiology report across different imaging modalities and hospitals.MethodsIn this institutional review board-approved retrospective study, we collated a dataset of CT, US, and MRI radiology reports from the University of California San Francisco Medical Center (UCSFMC) (n = 372,716) and the Zuckerberg San Francisco General (ZSFG) Hospital and Trauma Center (n = 60,049), both under a single institution. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, an automatic natural language evaluation metric that measures word overlap, was used for automatic natural language evaluation. A reader study with five cardiothoracic radiologists was performed to more strictly evaluate the model’s performance on a specific modality (CT chest exams) with a radiologist subspecialist baseline. We stratified the results of the reader performance study based on the diagnosis category and the original impression length to gauge case complexity.ResultsThe LLM achieved ROUGE-L scores of 46.51, 44.2, and 50.96 on UCSFMC and upon external validation, ROUGE-L scores of 40.74, 37.89, and 24.61 on ZSFG across the CT, US, and MRI modalities respectively, implying a substantial degree of overlap between the model-generated impressions and impressions written by the subspecialist attending radiologists, but with a degree of degradation upon external validation. In our reader study, the model-generated impressions achieved overall mean scores of 3.56/4, 3.92/4, 3.37/4, 18.29 s,12.32 words, and 84 while the original impression written by a subspecialist radiologist achieved overall mean scores of 3.75/4, 3.87/4, 3.54/4, 12.2 s, 5.74 words, and 89 for clinical accuracy, grammatical accuracy, stylistic quality, edit time, edit distance, and ROUGE-L score respectively. The LLM achieved the highest clinical accuracy ratings for acute/emergent findings and on shorter impressions.ConclusionsAn open-source fine-tuned LLM can generate impressions to a satisfactory level of clinical accuracy, grammatical accuracy, and stylistic quality. Our reader performance study demonstrates the potential of large language models in drafting radiology report impressions that can aid in streamlining radiologists’ workflows.