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Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting., (© 2022. The Author(s).)

The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis. We showed how we could give experience points to trainees based on annotation precision compared to ground truth radiologists' annotation, ranked in a live leaderboard. We believe that gamification elements could provide an engaging solution for radiology education. Our project was awarded first place out of eight participating hackathon teams., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

There is consistent demand for clinical exposure from students interested in radiology; however, the COVID-19 pandemic resulted in fewer available options and limited student access to radiology departments. Additionally, there is increased demand for radiologists to manage more complex quantification in reports on patients enrolled in clinical trials. We present an online educational curriculum that addresses both of these gaps by virtually immersing students (radiology preprocessors, or RPs) into radiologists' workflows where they identify and measure target lesions in advance of radiologists, streamlining report quantification. RPs switched to remote work at the beginning of the COVID-19 pandemic in our National Institutes of Health (NIH). We accommodated them by transitioning our curriculum on cross-sectional anatomy and advanced PACS tools to a publicly available online curriculum. We describe collaborations between multiple academic research centers and industry through contributions of academic content to this curriculum. Further, we describe how we objectively assess educational effectiveness with cross-sectional anatomical quizzes and decreasing RP miss rates as they gain experience. Our RP curriculum generated significant interest evidenced by a dozen academic and research institutes providing online presentations including radiology modality basics and quantification in clinical trials. We report a decrease in RP miss rate percentage, including one virtual RP over a period of 1 year. Results reflect training effectiveness through decreased discrepancies with radiologist reports and improved tumor identification over time. We present our RP curriculum and multicenter experience as a pilot experience in a clinical trial research setting. Students are able to obtain useful clinical radiology experience in a virtual learning environment by immersing themselves into a clinical radiologist's workflow. At the same time, they help radiologists improve patient care with more valuable quantitative reports, previously shown to improve radiologist efficiency. Students identify and measure lesions in clinical trials before radiologists, and then review their reports for self-evaluation based on included measurements from the radiologists. We consider our virtual approach as a supplement to student education while providing a model for how artificial intelligence will improve patient care with more consistent quantification while improving radiologist efficiency., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients." This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records., (© 2021. The Author(s).)

To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.

Purpose: Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis., Methods: A main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods., Results: For discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient., Conclusion: Our results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays.

Oncologists evaluate therapeutic response in cancer trials based on tumor quantification following selected "target" lesions over time. At our cancer center, a majority of oncologists use Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 quantifying tumor progression based on lesion measurements on imaging. Currently, our oncologists handwrite tumor measurements, followed by multiple manual data transfers; however, our Picture Archiving Communication System (PACS) (Carestream Health, Rochester, NY) has the ability to export tumor measurements, making it possible to manage tumor metadata digitally. We developed an interface, "Exportable Notation and Bookmark List Engine" (ENABLE), which produces prepopulated RECIST v1.1 worksheets and compiles cohort data and data models from PACS measurement data, thus eliminating handwriting and manual data transcription. We compared RECIST v1.1 data from eight patients (16 computed tomography exams) enrolled in an IRB-approved therapeutic trial with ENABLE outputs: 10 data fields with a total of 194 data points. All data in ENABLE's output matched with the existing data. Seven staff were taught how to use the interface with a 5-min explanatory instructional video. All were able to use ENABLE successfully without additional guidance. We additionally assessed 42 metastatic genitourinary cancer patients with available RECIST data within PACS to produce a best response waterfall plot. ENABLE manages tumor measurements and associated metadata exported from PACS, producing forms and data models compatible with cancer databases, obviating handwriting and the manual re-entry of data. Automation should reduce transcription errors and improve efficiency and the auditing process.

A methodology is described using Adobe Photoshop and Adobe Extendscript to process DICOM images with a Relative Attenuation-Dependent Image Overlay (RADIO) algorithm to visualize the full dynamic range of CT in one view, without requiring a change in window and level settings. The potential clinical uses for such an algorithm are described in a pictorial overview, including applications in emergency radiology, oncologic imaging, and nuclear medicine and molecular imaging.

We present an open-source, picture archiving and communication system (PACS)-integrated radiation exposure extraction engine (RE3) that provides study-, series-, and slice-specific data for automated monitoring of computed tomography (CT) radiation exposure. RE3 was built using open-source components and seamlessly integrates with the PACS. RE3 calculations of dose length product (DLP) from the Digital imaging and communications in medicine (DICOM) headers showed high agreement (R (2) = 0.99) with the vendor dose pages. For study-specific outlier detection, RE3 constructs robust, automatically updating multivariable regression models to predict DLP in the context of patient gender and age, scan length, water-equivalent diameter (D w), and scanned body volume (SBV). As proof of concept, the model was trained on 811 CT chest, abdomen + pelvis (CAP) exams and 29 outliers were detected. The continuous variables used in the outlier detection model were scan length (R (2)  = 0.45), D w (R (2) = 0.70), SBV (R (2) = 0.80), and age (R (2) = 0.01). The categorical variables were gender (male average 1182.7 ± 26.3 and female 1047.1 ± 26.9 mGy cm) and pediatric status (pediatric average 710.7 ± 73.6 mGy cm and adult 1134.5 ± 19.3 mGy cm).

The Response Evaluation Criteria in Solid Tumors (RECIST) is the current standard for assessing therapy response in patients with malignant solid tumors; however, volumetric assessments are thought to be more representative of actual tumor size and hence superior in predicting patient outcomes. We segmented all primary and metastatic lesions in 21 chordoma patients for comparison to RECIST. Primary tumors were segmented on MR and validated by a neuroradiologist. Metastatic lesions were segmented on CT and validated by a general radiologist. We estimated times for a research assistant to segment all primary and metastatic chordoma lesions using semi-automated volumetric segmentation tools available within our PACS (v12.0, Carestream, Rochester, NY), as well as time required for radiologists to validate the segmentations. We also report success rates of semi-automatic segmentation in metastatic lesions on CT and time required to export data. Furthermore, we discuss the feasibility of volumetric segmentation workflow in research and clinical settings. The research assistant spent approximately 65 h segmenting 435 lesions in 21 patients. This resulted in 1349 total segmentations (average 2.89 min per lesion) and over 13,000 data points. Combined time for the neuroradiologist and general radiologist to validate segmentations was 45.7 min per patient. Exportation time for all patients totaled only 6 h, providing time-saving opportunities for data managers and oncologists. Perhaps cost-neutral resource reallocation can help acquire volumes paralleling our example workflow. Our results will provide researchers with benchmark resources required for volumetric assessments within PACS and help prepare institutions for future volumetric assessment criteria.

Purpose: To improve detection of pulmonary and pleural abnormalities caused by pneumonia or tuberculosis (TB) in digital chest X-rays (CXRs)., Methods: A method was developed and tested by combining shape and texture features to classify CXRs into two categories: TB and non-TB cases. Based on observation that radiologist interpretation is typically comparative: between left and right lung fields, the algorithm uses shape features to describe the overall geometrical characteristics of the lung fields and texture features to represent image characteristics inside them., Results: Our algorithm was evaluated on two different datasets containing tuberculosis and pneumonia cases., Conclusions: Using our proposed algorithm, we were able to increase the overall performance, measured as area under the (ROC) curve (AUC) by 2.4 % over our previous work.

This study aims to evaluate the utility of compressed computed tomography (CT) studies (to expedite transmission) using Motion Pictures Experts Group, Layer 4 (MPEG-4) movie formatting in combat hospitals when guiding major treatment regimens. This retrospective analysis was approved by Walter Reed Army Medical Center institutional review board with a waiver for the informed consent requirement. Twenty-five CT chest, abdomen, and pelvis exams were converted from Digital Imaging and Communications in Medicine to MPEG-4 movie format at various compression ratios. Three board-certified radiologists reviewed various levels of compression on emergent CT findings on 25 combat casualties and compared with the interpretation of the original series. A Universal Trauma Window was selected at -200 HU level and 1,500 HU width, then compressed at three lossy levels. Sensitivities and specificities for each reviewer were calculated along with 95 % confidence intervals using the method of general estimating equations. The compression ratios compared were 171:1, 86:1, and 41:1 with combined sensitivities of 90 % (95 % confidence interval, 79-95), 94 % (87-97), and 100 % (93-100), respectively. Combined specificities were 100 % (85-100), 100 % (85-100), and 96 % (78-99), respectively. The introduction of CT in combat hospitals with increasing detectors and image data in recent military operations has increased the need for effective teleradiology; mandating compression technology. Image compression is currently used to transmit images from combat hospital to tertiary care centers with subspecialists and our study demonstrates MPEG-4 technology as a reasonable means of achieving such compression.

The authors present a unique medical technical application for illustrating the success and/or failure of the physiological healing process as a dynamically morphed video. Two examples used in this report include the healing of a severely fractured humerus from an explosion in Iraq and the other of dramatic tissue destruction from a poisonous spider bite. For the humerus, several sequential x-rays obtained throughout orthopedic surgical procedures and the healing process were morphed together representing a time-lapsed video of the healing process. The end result is a video that demonstrates the healing process in an animation that radiologists envision and report to other clinicians. For the brown recluse spider bite, a seemingly benign skin lesion transforms into a wide gaping necrotic wound with dramatic appearance within days. This novel technique is not presented for readily apparent clinical advantage, rather, it may have more immediate application in providing treatment options to referring providers and/or patients, as well as educational value of healing or disease progression over time. Image morphing is one of those innovations that is just starting to come into its own. Morphing is an image processing technology that transforms one image into another by generating a series of intermediate synthetic images. It is the same process that Hollywood uses to turn people into animals in movies, for example. The ability to perform morphing, once restricted to high-end graphics workstations, is now widely available for desktop computers. The authors describe how a series of radiographic images were morphed into a short movie clip using readily available software and an average laptop. The resultant video showed the healing process of an open comminuted humerus fracture that helped demonstrate how amazingly the human body heals in a case presentation in a time-lapse fashion.