IODeep: An IOD for the introduction of deep learning in the DICOM standard.

BACKGROUND AND OBJECTIVE: In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice even if they could support effectively the physician during the diagnostic process. Apart from the issues related to the explainability of the predictions of a neural model, such systems are not integrated in the diagnostic workflow, and a standardization of their use is needed to achieve this goal. METHODS: This paper presents IODeep a new DICOM Information Object Definition (IOD) aimed at storing both the weights and the architecture of a DNN already trained on a particular image dataset that is labeled as regards the acquisition modality, the anatomical region, and the disease under investigation. RESULTS: The IOD architecture is presented along with a DNN selection algorithm from the PACS server based on the labels outlined above, and a simple PACS viewer purposely designed for demonstrating the effectiveness of the DICOM integration, while no modifications are required on the PACS server side. Also a service based architecture in support of the entire workflow has been implemented. CONCLUSION: IODeep ensures full integration of a trained AI model in a DICOM infrastructure, and it is also enables a scenario where a trained model can be either fine-tuned with hospital data or trained in a federated learning scheme shared by different hospitals. In this way AI models can be tailored to the real data produced by a Radiology ward thus improving the physician decision making process. Source code is freely available at https://github.com/CHILab1/IODeep.git.

[Construction of a Temporary Radiological Image Viewing Network for Large-scale Disasters].

The Ministry of Health, Labor and Welfare mandated the creation of the business continuity plan (BCP) for disaster key hospitals on March 31, 2017. Supposing the hospital information system (HIS) failure occurred, the picture archiving and communication system (PACS) also suffers obstacles, we assumed building a new network was necessary for radiological examination images. The purpose of this study was to investigate whether building a new network for radiological examination images is necessary in an emergency. Using wireless fidelity (Wi-Fi), the new network consisting of one image server and two tablet terminals A and B was constructed. The study measured the portable image transfer time for various stages of the network. The results were as follows: Transfer time from the mobile X-ray unit to the image server was 4.12±0.86 s, that from the image server to the tablet device A was 5.14±0.71 s, and that from the image server to the tablet device B was 7.32±1.66 s. Therefore, the new network configuration can provide a reliable means of accessing radiological images during emergency situations when the HIS and PACS may experience obstacles or failures.

Generative Large Language Models for Detection of Speech Recognition Errors in Radiology Reports.

This study evaluated the ability of generative large language models (LLMs) to detect speech recognition errors in radiology reports. A dataset of 3233 CT and MRI reports was assessed by radiologists for speech recognition errors. Errors were categorized as clinically significant or not clinically significant. Performances of five generative LLMs-GPT-3.5-turbo, GPT-4, text-davinci-003, Llama-v2-70B-chat, and Bard-were compared in detecting these errors, using manual error detection as the reference standard. Prompt engineering was used to optimize model performance. GPT-4 demonstrated high accuracy in detecting clinically significant errors (precision, 76.9%; recall, 100%; F1 score, 86.9%) and not clinically significant errors (precision, 93.9%; recall, 94.7%; F1 score, 94.3%). Text-davinci-003 achieved F1 scores of 72% and 46.6% for clinically significant and not clinically significant errors, respectively. GPT-3.5-turbo obtained 59.1% and 32.2% F1 scores, while Llama-v2-70B-chat scored 72.8% and 47.7%. Bard showed the lowest accuracy, with F1 scores of 47.5% and 20.9%. GPT-4 effectively identified challenging errors of nonsense phrases and internally inconsistent statements. Longer reports, resident dictation, and overnight shifts were associated with higher error rates. In conclusion, advanced generative LLMs show potential for automatic detection of speech recognition errors in radiology reports. Keywords: CT, Large Language Model, Machine Learning, MRI, Natural Language Processing, Radiology Reports, Speech, Unsupervised Learning Supplemental material is available for this article.

From omission to excellence: Reducing the percentage of incomplete trainee reports by ensuring documentation of comparison studies.

The inclusion of comparison studies within radiology reports is an important, standard practice. Despite this, we identified that after-hours preliminary reports rendered by trainees within our institution often omitted reference to comparison studies for pediatric inpatient portable radiographs. We addressed this issue through a quality improvement project targeting pediatric radiographs. Key interventions included modifying the structured reports by removing default text in the comparison field, designating the comparison field as mandatory, and restructuring the report templates to remove extraneous information. We also initiated a targeted educational campaign. 392 reports before and 267 reports after intervention (total 732 reports) were evaluated to determine the number of reports lacking comparison information when comparisons were available. Following the interventions, there was a statistically significant decrease in incomplete reports from 12.5% to 6%. This project highlights the success of utilizing structured reporting to improve the quality of trainee reports.

Katherine P. Andriole - A trailblazer in medical informatics and radiology.

In a traditionally male-dominated field, the journey of Dr. Andriole represents a pioneering path in the realms of radiology and medical imaging informatics. Her career has not only reshaped the landscape of radiology but also championed diversity, equity, and inclusion in healthcare technology. Through a comprehensive exploration of Dr. Andriole's career trajectory, we navigate her transition from analog to digital radiology, her influential role in pioneering picture archiving communication systems (PACS), and her dedication to mentorship and education in the field. Dr. Andriole's journey underscores the growing influence of women in radiology and informatics, exemplified by her Gold Medal accolades from esteemed organizations. Dr. Andriole's career serves as a beacon for aspiring radiologists and informaticians, emphasizing the significance of passion, mentorship, and collaborative teamwork in advancing the fields of radiology and informatics.

Quick Guide: Programming a Gaming Keyboard for PACS to Optimize Radiology Workflow.

The surging demand for diagnostic imaging has highlighted inefficiencies with traditional input devices. Radiologists, using conventional mice and keyboards, grapple with cumbersome shortcuts leading to fatigue, errors, and possible injuries. Gaming keyboards, designed for gamers' precision and adaptability, feature customizable keys that simplify complex tasks into single-touch actions, offering radiologists a more efficient workflow with less physical and mental strain. Incorporating these keyboards could revolutionize radiologists' engagement with PACS. The customizable feature significantly trims time spent searching, ushering in swifter, ergonomic interactions. This manuscript delineates a guide for adapting a Logitech gaming keyboard to radiology needs, from profile creations and shortcut mapping to intricate macro setups. Although the guide uses a Logitech gaming keyboard for demonstration, it is designed to be intuitive, helping users adapt to their unique needs across different modalities, subspecialties, and various radiology viewer software. Furthermore, its fundamental concepts are transferrable to other mouse brands or models with similar customization software. As radiology pivots toward utmost efficiency, gaming keyboards emerge as invaluable assets, promising significant workflow enhancements.

Thirty Years of the DICOM Standard.

Digital Imaging and Communications in Medicine (DICOM) is an international standard that defines a format for storing medical images and a protocol to enable and facilitate data communication among medical imaging systems. The DICOM standard has been instrumental in transforming the medical imaging world over the last three decades. Its adoption has been a significant experience for manufacturers, healthcare users, and research scientists. In this review, thirty years after introducing the standard, we discuss the innovation, advantages, and limitations of adopting the DICOM and its possible future directions.

Quick Guide: Programming a Gaming Mouse for PACS to Optimize Radiology Workflow.

The increasing demand for diagnostic imaging has added to the radiologists' workload, highlighting the shortcomings of conventional computer mice. Radiologists grapple with inefficiencies from frequent mouse clicks and keyboard shortcuts required for various PACS functions. These inefficiencies contribute to cognitive strain, errors, and repetitive strain injuries. High-performance gaming mice, known for their precision in the gaming world, offer multiple custom buttons and superior tracking. These features can streamline radiology tasks. Utilizing a gaming mouse tailored for radiology tasks can substantially enhance efficiency. Our guide offers a step-by-step approach to harnessing the gaming mouse's capabilities for radiology tasks, ensuring radiologists can enhance their workflow and minimize injury risks. Although the guide uses a Logitech gaming mouse for demonstration, it is designed to be intuitive, helping users adapt to their unique needs across different modalities, subspecialties, and various radiology viewer software. Importantly, its fundamental concepts are transferrable to other mouse brands or models with similar customization software.

Eye tracking validation: Improving radiologist reporting and interpretation.

RATIONALE AND OBJECTIVES: This study aims to validate a new radiology reporting style using eye tracking to maximize radiologist interpretation time, increase accuracy, and minimize dictation time, ultimately providing a clinically relevant, concise, and accurate reporting style. MATERIALS AND METHODS: The positive findings only dictation style using a podcast stand-alone microphone (n = 76) was compared with the standard check-list dictation style using a handheld microphone (n = 81). Experienced board-certified radiologists used each style for various imaging modalities. The number of voice recognition corrections per case was tracked. Eye-tracking glasses captured eye movement to document dictation, interpretation, and total examination times. This device also generated thermal heat maps for each style. The statistical difference between the two methods was assessed via descriptive analysis and inferential statistics. RESULTS: Eye tracking revealed that the new positive findings dictation style led to a noteworthy shift in radiologists' visual attention, with reduced heat map overlaying the reporting software compared to the standard check-list style, indicating greater focus on medical images. Cases with at least one voice recognition correction significantly decreased using the positive findings dictation style versus the standard check-list style (5.26 % vs. 14.81 %; p = 0.0240). The positive findings dictation style significantly decreased average dictation time (16.54 s [s] vs. 29.39 s; p = 0.0003) without impacting interpretation time (70.90 s vs. 64.30 s; p = 0.7799) or total examination time (87.45 s vs. 93.69 s; p = 0.3756) compared to the standard style. CONCLUSION: Positive findings only dictation style significantly decreased dictation time and enhanced accuracy without compromising total interpretation time.

Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging.

BACKGROUND: Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE: This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging. METHODS AND MATERIALS: This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy. RESULTS: Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors. CONCLUSIONS: This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

Personalizable AI platform for universal access to research and diagnosis in digital pathology.

BACKGROUND AND MOTIVATION: Digital pathology has been evolving over the last years, proposing significant workflow advantages that have fostered its adoption in professional environments. Patient clinical and image data are readily available in remote data banks that can be consumed efficiently over standard communication technologies. The appearance of new imaging techniques and advanced artificial intelligence algorithms has significantly reduced the burden on medical professionals by speeding up the screening process. Despite these advancements, the usage of digital pathology in professional environments has been slowed down by poor interoperability between services resulting from a lack of standard interfaces and integrative solutions. This work addresses this issue by proposing a cloud-based digital pathology platform built on standard and open interfaces. METHODS: The work proposes and describes a vendor-neutral platform that provides interfaces for managing digital slides, and medical reports, and integrating digital image analysis services compatible with existing standards. The solution integrates the open-source plugin-based Dicoogle PACS for interoperability and extensibility, which grants the proposed solution great feature customization. RESULTS: The solution was developed in collaboration with iPATH research project partners, including the validation by medical pathologists. The result is a pure Web collaborative framework that supports both research and production environments. A total of 566 digital slides from different pathologies were successfully uploaded to the platform. Using the integration interfaces, a mitosis detection algorithm was successfully installed into the platform, and it was trained with 2400 annotations collected from breast carcinoma images. CONCLUSION: Interoperability is a key factor when discussing digital pathology solutions, as it facilitates their integration into existing institutions' information systems. Moreover, it improves data sharing and integration of third-party services such as image analysis services, which have become relevant in today's digital pathology workflow. The proposed solution fully embraces the DICOM standard for digital pathology, presenting an interoperable cloud-based solution that provides great feature customization thanks to its extensible architecture.

Successes and challenges in extracting information from DICOM image databases for audit and research.

In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.

Measurement of radiologist reporting times: Assessment of precision, comparison of three different measurement techniques and review of potential applications.

INTRODUCTION: Radiologist reporting times are a key component of radiology department workload assessment, but reliable measurement remains challenging. Currently, there are three contenders for this task: median reporting times (MRTs), extracted directly from a department's radiology information system (RIS); study-ascribed times (SATs), using published tables of individual descriptors derived from a combination of measurement and consensus; and radiology reporting figures (RRFs), using published tables of measured times based on modality and numbers of anatomical areas. METHODS: We review these techniques, their possible uses and some potential pitfalls. We discuss the level of precision that can realistically be attained in measuring reporting times, and list the strengths and weaknesses of each technique, comparing them in relation to each of eight potential applications. RESULTS: We believe that SATs are challenging for practical use due to their static nature, absent common descriptors and large number. RRFs are more user-friendly but are also static and require ongoing updates; currently, they do not include ultrasound. MRTs cannot currently be extracted from every RIS, but where available they are easy to use and their dynamic nature provides the most objective data. They underestimate the unmeasurable components of a radiologist's work and therefore the total time spent in a reporting session. CONCLUSION: MRTs are superior to the other methods in flexibility, precision and ease of use. All institutions should have access to this data and we call on vendors of Radiology Information Systems which are currently not capable of providing it to make the necessary modifications.

Assessing Available Open-Source PACS Options.

Medical imaging technology is producing a growing number of medical images types as well as patient-related information. The benefits of using modern medical imaging systems in healthcare are undeniable. Picture archiving and communication system (PACS) have revolutionized medical imaging practice. PACS have widely impacted the accessibility of medical images, reduced imaging costs, eliminated the physical storage of films, improved time management of radiologists, and allowed automated decision-making and diagnosis. Many health organizations and manufacturers have invested on developing commercial PACS. However, commercial PACS are not affordable for all hospitals while open-source PACS are increasingly becoming a viable option. Our research project is looking for an open-source PACS for the Donka University hospital of Guinea. Open-source PACS are currently available and are offering varying functionalities, documentation, and technical support from their developer communities. Selecting an open-source PACS is not an easy task and not only depends on the hospital requirements but also requires assessing each open-source PACS to find the best match. In this paper, the most popular open-source PACS are evaluated using a simple comparison approach based on four criteria. The result of this assessment shows that Orthanc, DCM4CHE, DCMTK, and Dicoogle are the most mature open-source PACS according to our criteria and the needs of Donka.

Challenges, Barriers, and Successes of Standardized Report Templates: Results of a Society of Interventional Radiology Survey.

Registry data are being increasingly used to establish treatment guidelines, set benchmarks, allocate resources, and make payment decisions. Although many registries rely on manual data entry, the Society of Interventional Radiology (SIR) is using automated data extraction for its VIRTEX registry. This process relies on participants using consistent terminology with highly structured data in physician-developed standardized reports (SR). To better understand barriers to adoption, a survey was sent to 3,178 SIR members. Responses were obtained from 451 interventional radiology practitioners (14.2%) from 92 unique academic and 151 unique private practices. Of these, 75% used structured reports and 32% used the SIR SR. The most common barriers to the use of these reports include SR length (35% of respondents), lack of awareness about the SR (31%), and lack of agreement on adoption within practices (27%). The results demonstrated insights regarding barriers in the use and/or adoption of SR and potential solutions.

Working from home: Changes in radiologist reporting behavior in response to the COVID-19 pandemic.

BACKGROUND: Remote reporting is an important preventive measure against coronavirus disease 2019 (COVID-19) for radiology departments; it reduces the chance of cross-infections between coworkers. The purpose of this study was to evaluate how the preferred locations that radiologists filed reports from changed in response to COVID-19 by measuring the use of internal teleradiology workstations. METHODS: Data were obtained from the radiological information system (RIS) database at our institution, which recorded the reporting workstation for each radiological examination. The reporting activities in 2021 were divided into computed radiography (CR) and computed tomography (CT)/magnetic resonance imaging (MRI) groups. The Wilcoxon signed-rank test was used to measure differences in the use of off-site workstations in prepandemic, midpandemic, and postpandemic periods. RESULTS: There were statistically significant increases in the number of reports filed from off-site workstations for each attending physician from the prepandemic period to the midpandemic period in both the CR (15.1%-25.4%, p = 0.041) and CT/MRI (18.9%-28.7%, p = 0.006) groups. There was no significant difference noted between the prepandemic and postpandemic periods for either the CR (15.1% vs 18.4%, p = 0.727) or CT/MRI group (18.9% vs 23.3%, p = 0.236). CONCLUSION: In response to the COVID-19 outbreak, radiologists used internal teleradiology to report CR and CT/MRI examinations significantly more frequently. In contrast to the predictions of previous studies, the use of internal teleradiology returned to baseline levels after the pandemic was under control.