Radiological Interpretation Research Articles

Technical feasibility of automated blur detection in digital mammography using convolutional neural network

Abstract Background The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mammography. Methods A retrospective dataset consisting of 152 examinations acquired with mammography machines from three different vendors was utilized. The blurred areas were contoured by expert breast radiologists. Normalized Wiener spectra (nWS) were extracted in a sliding window manner from each mammogram. These spectra served as input for a convolutional neural network (CNN) generating the probability of the spectra originating from a blurred region. The resulting blur probability mask, upon thresholding, facilitated the classification of a mammogram as either blurred or sharp. Ground truth for the test set was defined by the consensus of two radiologists. Results A significant correlation between the view (p < 0.001), as well as between the laterality and the presence of blur (p = 0.004) was identified. The developed model AUROC of 0.808 (95% confidence interval 0.794–0.821) aligned with the consensus in 78% (67–83%) of mammograms classified as blurred. For mammograms classified by consensus as sharp, the model achieved agreement in 75% (67–83%) of them. Conclusion A model for blur detection was developed and assessed. The results indicate that a robust approach to blur detection, based on feature extraction in frequency space, tailored to radiologist expertise regarding clinical relevance, could eliminate the subjectivity associated with the visual assessment. Relevance statement This blur detection model, if implemented in clinical practice, could provide instantaneous feedback to technicians, allowing for prompt mammogram retakes and ensuring that only high-quality mammograms are sent for screening and diagnostic tasks. Key Points Blurring in mammography limits radiologist interpretation and diagnostic accuracy. This objective blur detection tool ensures image quality, and reduces retakes and unnecessary exposures. Wiener spectrum analysis and CNN enabled automated blur detection in mammography. Graphical Abstract

Analysis of panel physician inquiries to U.S. TB Centers of Excellence, 2018-2022.

Applicants seeking entry into the United States are examined overseas for TB by panel physicians and international immigration clinicians guided by Centers for Disease Control and Prevention (CDC) TB Technical Instructions. To support this effort, CDC-funded TB Centers of Excellence (COEs) provide web-based expert consultation, with documentation stored in a medical consultation database (MCD). MCD analysis can reveal inquiry trends among panel physicians worldwide. TB-related queries in the COE MCD from January 1, 2018, to December 31, 2022, were analyzed using a descriptive coding scheme developed through inductive analysis, allowing multiple themes per entry. A total of 215 queries from 126 patients in 28 countries were analyzed. Major themes included evaluating diagnostic criteria, tailoring treatment, and managing comorbidities or adverse reactions. Diagnostic questions (n = 104, 48.4%) included mycobacterial culture, smear, and radiology interpretation. Treatment tailoring inquiries involved optimizing the initial regimen (n = 89, 41.4%) or modifying existing regimens (n = 26, 12.1%). Additionally, 50 consultations (23.2%) mentioned comorbidities, while 47 (21.9%) described adverse reactions. The MCD analysis identified topics where TB expertise was provided in overseas medical evaluation. These topics highlight opportunities for targeted panel physician education to improve the health of individual applicants and advance U.S. TB elimination efforts.

Gamma Dose Rate Measurements in Northern Spain: Influence of Local Meteorological Scenarios on Radiological "False Alarms" in a Real-Time Radiological Monitoring Network.

The present study characterizes gamma dose rate (GDR) concentrations in Bilbao, located in the northern Iberian Peninsula, utilizing a comprehensive 10-min interval database spanning from 2009 to 2018. This station belongs to the radiological environmental monitoring of the Basque Country network. The daily average GDR was found to be 0.07624 ± 0.00004 µSv/h, with the daily 95th percentile averaging 0.08026 ± 0.00007 µSv/h throughout the entire period. Our analysis specifically addresses the impact of precipitation on GDR, revealing a higher correlation coefficient for daily 95th percentile values compared to daily averages. Additionally, the influence of the Galerna (GL) event, a natural meteorological phenomenon in this region, on GDR was investigated, noting that it can develop both with and without precipitation. Understanding the interaction between GDR and this meteorological scenario is vital for the development of more reliable radiological monitoring strategies and for safeguarding public health. For this purpose, 40 GL events were analyzed. The present results indicate that GDR values frequently exceed alarm levels when a GL is formed. These GDR peaks should be considered natural radiological events, necessitating the classification of such GDR peaks as false alarms within the radiological monitoring network. To explain them in detail, 10-min time series of precipitation and radon outdoor concentrations were analyzed. The results demonstrate that the GL event with precipitation is a meteorological scenario that can be associated with false alarms. The present analysis provides a distinct contrast in radon behavior under the same meteorological event in case of being developed with precipitation or without precipitation. The findings from this analysis are crucial for avoiding and understanding false radiological alarms triggered in the monitoring network, thereby enhancing the accuracy of radiological data interpretation and improving public safety measures.

Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoder

Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. Commonly used attribution methods localize image areas important for CNN prediction but do not characterize relevant imaging features underlying these areas, acting as a barrier to the adoption of CNNs for clinical use. We therefore propose Semantic Exploration and Explainability using a Style-based Generative Adversarial Autoencoder Network (SEE-GAAN), an explainability framework that uses latent space manipulation to generate a sequence of synthetic images that semantically visualizes how clinical and CNN features manifest within medical images. Visual analysis of changes in these sequences then facilitates the interpretation of features, thereby improving explainability. SEE-GAAN was first developed on a cohort of 26,664 chest radiographs across 15,409 patients from our institution. SEE-GAAN sequences were then generated across several clinical features and CNN predictions of NT-pro B-type natriuretic peptide (BNPP) as a proxy for acute heart failure. Radiological interpretations indicated SEE-GAAN sequences captured relevant changes in anatomical and pathological morphology associated with clinical and CNN predictions and clarified ambiguous areas highlighted by commonly used attribution methods. Our study demonstrates SEE-GAAN can facilitate our understanding of clinical features for imaging biomarker exploration and improve CNN transparency over commonly used explainability methods.

Revolution or risk?-Assessing the potential and challenges of GPT-4V in radiologic image interpretation.

ChatGPT-4 Vision (GPT-4V) is a state-of-the-art multimodal large language model (LLM) that may be queried using images. We aimed to evaluate the tool's diagnostic performance when autonomously assessing clinical imaging studies. A total of 206 imaging studies (i.e., radiography (n = 60), CT (n = 60), MRI (n = 60), and angiography (n = 26)) with unequivocal findings and established reference diagnoses from the radiologic practice of a large university hospital were accessed. Readings were performed uncontextualized, with only the image provided, and contextualized, with additional clinical and demographic information. Responses were assessed along multiple diagnostic dimensions and analyzed using appropriate statistical tests. With its pronounced propensity to favor context over image information, the tool's diagnostic accuracy improved from 8.3% (uncontextualized) to 29.1% (contextualized, first diagnosis correct) and 63.6% (contextualized, correct diagnosis among differential diagnoses) (p ≤ 0.001, Cochran's Q test). Diagnostic accuracy declined by up to 30% when 20 images were re-read after 30 and 90 days and seemed unrelated to the tool's self-reported confidence (Spearman's ρ = 0.117 (p = 0.776)). While the described imaging findings matched the suggested diagnoses in 92.7%, indicating valid diagnostic reasoning, the tool fabricated 258 imaging findings in 412 responses and misidentified imaging modalities or anatomic regions in 65 images. GPT-4V, in its current form, cannot reliably interpret radiologic images. Its tendency to disregard the image, fabricate findings, and misidentify details, especially without clinical context, may misguide healthcare providers and put patients at risk. Question Can Generative Pre-trained Transformer 4 Vision (GPT-4V) interpret radiologic images-with and without clinical context? Findings GPT-4V performed poorly, demonstrating diagnostic accuracy rates of 8% (uncontextualized), 29% (contextualized, most likely diagnosis correct), and 64% (contextualized, correct diagnosis among differential diagnoses). Clinical relevance The utility of commercial multimodal large language models, such as GPT-4V, in radiologic practice is limited. Without clinical context, diagnostic errors and fabricated findings may compromise patient safety and misguide clinical decision-making. These models must be further refined to be beneficial.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Characteristics of the Reciprocal Movement of Radiographers’ Gaze Based on a Comparison of Entry-Level and Experienced Radiographers

With the development of AI, AI may substitute for physicians in radiological interpretations in the future. The purpose of this paper is to identify factors that could form the foundation for algorithms enabling AI to explore pathological findings. This study focuses on the "angles of gaze trajectory" and "average speeds of gaze movement," and conducts a comparative analysis between entry-level and experienced radiographers. Firstly, based on the results of angles of gaze movement, experienced radiographers exhibited frequent movements around the 180-degree mark compared to entry-level radiographer. Both entrylevel and experienced radiographers commonly displayed frequent movements around 0 degrees and near 350 degrees. Secondly, according to the results of average speeds of gaze movement, entry-level radiographers were found to have faster movement speeds compared to experienced radiographers.

Improvement of quality of life in women ≤ 25-years-old with chronic pelvic pain following stenting of nonthrombotic iliac vein compression.

Stenting of nonthrombotic iliac vein compression for chronic pelvic pain (CPP) is controversial, especially in young populations. The current study evaluated the effects of iliac vein stenting on CPP through the assessment of quality-of-life (QoL) and pain scores in female patients ≤25-year-old. A retrospective single site cohort study was conducted for 30 female patients who were treated with left common iliac vein (LCIV) stenting for CPP. QoL was assessed before and after stenting using the following assessments: International Pelvic Pain Society (IPPS), Pelvic Congestion Syndrome (PCS), Pelvic Pain and Urinary Urgency Frequency (PUF), Rome III IBS, ancillary symptoms assessment, and pain scores. Secondary evaluation of the accuracy of radiologic interpretations of iliac vein compression on CT and MRI was also performed. Finally, commonly associated comorbidities within this population were assessed. Patient reported outcomes in 30 iliac vein stent patients demonstrated improvements in QoL post-stenting as demonstrated through significant reductions in IPPS, PCS, and pain scores (p = .0103, .0156, and .0092, respectively). Many of the cross-sectional imaging studies that were read by the interpreting radiologist as normal or mild compression were later identified as moderate or severe by the interventional radiologist clinically evaluating the patient. These patients went on to show significant compression on venography and intravascular ultrasound with associated clinical improvement after stenting. Significant improvements in QoL were identified in young women with CPP after LCIV stenting. Similarities in the clinical presentation of common comorbidities and misinterpretation of cross-sectional imaging studies may make the identification of pelvic venous disease more challenging. Our findings endorse the need for further research on stenting in young women with venous origin CPP through comparative outcomes studies and blinded randomized controlled trials.

The “Hungry Judge” effect on prostate MRI reporting: Chronobiological trends from 35’004 radiologist interpretations

AimTo investigate the associations between the hour of the day and Prostate Imaging–Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports. Materials and methodsRetrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model. ResultsA total of 35′004 prostate MRI interpretations by 26 radiologists were included. A significant association between the hour of day and mean PI-RADS score was identified (β2 = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day. ConclusionThese findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.

Variability in chest radiology interpretation between thoracic and non-thoracic radiologists: Implications for pulmonary fibrosis care

Variability in chest radiology interpretation between thoracic and non-thoracic radiologists: Implications for pulmonary fibrosis care

Concordance and Discordance Between Radiology Residents and Consultant Radiologist Interpretation Of CT Brain

OBJECTIVES The primary objective of this study is to assess the degree of concordance and discordance between the interpretations of computed tomography (CT) brain images by resident and consultant radiologists while emphasizing the critical significance of accurate image interpretation for informed clinical decision-making. METHODOLOGY The evaluation of radiology reports for CT Brain interpretation through a prospective analysis at the Radiology Department of Rehman Medical Institute over two years, from 1st October 2020 to 31st October 2022. A total of 198 patients who underwent cranial CT scans were interpreted by residents (R1, R2, R3, R4). Following this, the consultant radiologists reviewed the images and completed their reports. The reports of the residents and the consultant radiologists were then compared, and concordance was achieved when the residents’ reports were consistent with the final radiologist’s reports. The data collected were recorded in Microsoft Excel. The statistical analysis was performed using SPSS version 22 (IBM Corp., Armonk, NY), and the kappa coefficient was used to determine the level of agreement between residents and consultants.RESULTSAmong the 198 CT Head reports evaluated, 186 of them were in agreement with the final report of the consultant radiologist. Of the correctly diagnosed cases, R1 correctly diagnosed 46 cases, R2 correctly diagnosed 80 cases, R3 correctly diagnosed 54 cases, and R4 correctly diagnosed 6 cases. Our study achieved a percentage agreement of 93.93, with a Cohen's kappa coefficient of 0.8.CONCLUSIONThe overall concordance rate between residents and consultant radiologists was 93.93%, with a kappa coefficient 0.8. This high kappa coefficient indicates strong agreement between the two groups.

Role of teleradiology in the interpretation of ultrasound images acquired in the emergency setting

BACKGROUND: Teleradiology has become an important tool in emergency medicine, particularly in the interpretation of emergency ultrasonography. In emergency situations, where time is essential, rapid diagnosis and treatment can mean the difference between life and death. Teleradiology is an innovative alternative to augment the staffing and fill the gaps of onsite radiology personnel in emergency departments or during off-hours. AIM: To assess the effectiveness/impact of teleradiology in emergency ultrasound interpretation. MATERIALS AND METHODS: A retrospective study was performed in a cohort of 33,616 patients from 86 hospitals across the USA between January and December 2022. The study involved radiological interpretations of 37,253 ultrasound images acquired in the emergency setting by American Board Certified Radiologists empaneled by a teleradiology service provider, headquartered in Bangalore, India. RESULTS: The proposed telehealth model provided timely and quality reporting of 37,253 scans of patients with a mean turnaround time of 35.71 min (95% confidence interval 35.50–35.91). CONCLUSION: This study demonstrates that a structured telesonography program with defined protocols for image capture, transmission, and clinical communication can allow for successful immediate reporting of ultrasound data in the emergency care setting.

Implementation of a New Classification and Stratification System for Solitary Bone Tumor: Osseous Tumor Radiological and Interpretation and Management System (OT-RIMS)

Abstract Objectives To propose a histological-grades-based Osseous Tumor Radiological Interpretation and Management System (OT-RIMS) that would simplify the radiological evaluation of bone tumors, categorize key radiological features into severity levels, and inform corresponding patient management actions. Methods This retrospective study between January 2015 and August 2022 evaluated patients with solitary bone tumors confirmed by pathology and imaging follow-up received two or three imaging modalities of radiographs, CT, or MRI. Three radiologists independently assessed radiological features, categorized bone lesions based on OT-RIMS criteria, and reached a consensus. Kappa statistics and observed agreement were calculated. Results A total of 341 patients (mean age, 26.0 years; 159 women) were included, with 102 malignant, 177 benign, and 62 intermediate or low-grade malignant bone lesions. Sensitivity and specificity of readers 1, 2, and 3, respectively, in the identification of malignant tumors into OT-RIMS 4 were 93.1% (95 of 102) and 93.3% (223 of 239), 96.1% (98 of 102) and 91.6% (219 of 239), 92.2% (94 of 102) and 89.5% (214 of 239). Inter-reader agreement of OT-RIMS category for three readers was considered excellent (Kendall’s W = 0.924, p &amp;lt; 0.001) with a kappa value of reproducibility in categories 1&amp;2, 3, and 4 of 0.764, 0.528, and 0.930, respectively. Conclusion The OT-RIMS category demonstrated excellent reproducibility despite the reader’s expertise level in categorizing the risk stratification of bone tumors and informing patient management, with histological grades used as the reference standard. Advances in knowledge The OT-RIMS category reliably stratifies bone tumors into four categories corresponding to histological grades and standardized patient management.

Application of a deep learning algorithm for the diagnosis of HCC

Background & AimsHepatocellular carcinoma (HCC) is characterized by a high mortality rate. The Liver Imaging Reporting and Data System (LI-RADS) results in considerable proportions of indeterminate observations, rendering an accurate diagnosis difficult. MethodsWe developed four deep learning models for diagnosing HCC on computed tomography (CT) via a training-validation-testing approach. Thin-slice triphasic CT liver images and relevant clinical information were collected and processed for deep learning. HCC was diagnosed and verified via a 12-month clinical composite reference standard. CT observations among at-risk patients were annotated using LI-RADS. Diagnostic performance was assessed by internal validation and independent external testing. We conducted sensitivity analyses of different subgroups, deep learning explainability evaluation, and misclassification analysis. ResultsFrom 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under curves (AUCs) of 0.919 (95%CI 0.903-0.935) and 0.901 (95%CI 0.879-0.924) at the observation (n=1077) and patient (n=685) levels respectively during internal validation, compared to 0.839 (95%CI 0.814-0.864) and 0.822 (95%CI 0.790-0.853) respectively for standard-of-care radiological interpretation. ST3DCN’s negative predictive values were 0.966 (95%CI 0.954-0.979) and 0.951 (95%CI 0.931-0.971) respectively. ST3DCN’s observation-level AUCs among at-risk patients, 2-5 cm observations and singular porto-venous phase analysis were 0.899 (95%CI 0.874-0.924), 0.872 (95%CI 0.838-0.909) and 0.912 (95%CI 0.895-0.929) respectively. In external testing (551/717 patients/observations), ST3DCN’s AUC was 0.901 (95%CI 0.877-0.924), non-inferior to radiological interpretation (AUC 0.900, 95%CI 0.877-923). ConclusionsST3DCN achieved strong, robust performance for accurate HCC diagnosis on CT. Deep learning can expedite and improve the diagnostic process of HCC. Impact and implicationsThe clinical applicability of deep learning in HCC diagnosis is potentially huge, especially considering the expected increase in the incidence and mortality of HCC in Eastern Asia and worldwide. Early diagnosis through deep learning can lead to earlier definitive management, particularly for at-risk patients. The model can be broadly deployed for patients undergoing a triphasic contrast CT scan of the liver to reduce the current high mortality rate of HCC.

How Is Bone Regeneration Influenced by Polymer Membranes? Insight into the Histological and Radiological Point of View in the Literature.

The aim of this study was to analyze published works that investigate the in vivo bone regeneration capacity of polymeric membranes loaded with active substances and growth factors. This scoping review's purpose was to highlight the histological and radiological interpretation of the locally produced effects of the polymer membranes studied so far. For the selection of the articles, a search was made in the PubMed and ScienceDirect databases, according to the PRISMA algorithm, for research/clinical trial type studies. The search strategy was represented by the formula "((biodegradable scaffolds AND critical bone defect) OR (polymers AND mechanical properties) OR (3Dmaterials AND cytotoxicity) AND bone tissue regeneration)" for the PubMed database and "((biodegradable scaffolds AND polymers) OR (polymers AND critical bone defects) OR (biodegradable scaffolds AND mechanical properties) AND bone tissue regeneration)" for the ScienceDirect database. Ethical approval was not required. Eligibility criteria included eight clinical studies published between 2018 and 2023. Our analysis showed that polymer membranes that met most histopathological criteria also produced the most remarkable results observed radiologically. The top effective scaffolds were those containing active macromolecules released conditionally and staged. The PLGA and polycaprolactone scaffolds were found in this category; they granted a marked increase in bone density and improvement of osteoinduction. But, regardless of the membrane composition, all membranes implanted in created bone defects induced an inflammatory response in the first phase.

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists. We retrospectively included abdominopelvic CT images with the following inclusion criteria: a) CT images from patients with solid organ malignancies between March 1 and March 31, 2019, in a single institution; and b) abdominal CT images interpreted as negative for basal lung metastases. Reference standards for diagnosis of lung metastases were confirmed by reviewing medical records and subsequent CT images. An AI system that could automatically detect lung nodules on CT images was applied retrospectively. A radiologist reviewed the AI detection results to classify them as lesions with the possibility of metastasis or clearly benign. The performance of the initial AI results and the radiologist's review of the AI results were evaluated using patient-level and lesion-level sensitivities, false-positive rates, and the number of false-positive lesions per patient. A total of 878 patients (580 men; mean age, 63 years) were included, with overlooked basal lung metastases confirmed in 13 patients (1.5%). The AI exhibited an area under the receiver operating characteristic curve value of 0.911 for the identification of overlooked basal lung metastases. Patient- and lesion-level sensitivities of the AI system ranged from 69.2% to 92.3% and 46.2% to 92.3%, respectively. After a radiologist reviewed the AI results, the sensitivity remained unchanged. The false-positive rate and number of false-positive lesions per patient ranged from 5.8% to 27.6% and 0.1% to 0.5%, respectively. Radiologist reviews significantly reduced the false-positive rate (2.4%-12.6%; all P values < 0.001) and the number of false-positive lesions detected per patient (0.03-0.20, respectively). The AI system could accurately identify basal lung metastases detected in abdominopelvic CT images that were overlooked by radiologists, suggesting its potential as a tool for radiologist interpretation. The AI system can identify missed basal lung lesions in abdominopelvic CT scans in patients with malignancy, providing feedback to radiologists, which can reduce the risk of missing basal lung metastasis.

Enhancing Imaging Anatomy Competency: Integrating Digital Imaging and Communications in Medicine (DICOM) Viewers Into the Anatomy Lab Experience.

Radiologic interpretation is a skill necessary for all physicians to provide quality care for their patients. However, some medical students are not exposed to Digital Imaging and Communications in Medicine (DICOM) imaging manipulation until their third year during clinical rotations. The objective of this study is to evaluate how medical students exposed to DICOM manipulation perform on identifying anatomical structures compared to students who were not exposed. This was a cross-sectional cohort study with 19 medical student participants organized into a test and control group. The test group consisted of first-year students who had been exposed to a new imaging anatomy curriculum (n = 9). The control group consisted of second-year students who had not had this experience (n = 10). The outcomes measured included quiz performance, self-reported confidence levels, and eye-tracking data. Students in the test group performed better on the quiz compared to students in the control group (p = 0.03). Confidence between the test and control groups was not significantly different (p = 0.16), though a moderate to large effect size difference was noted (Hedges' g = 0.75). Saccade peak velocity and fixation duration between the groups were not significantly different (p = 0.29, p = 0.77), though a moderate effect size improvement was noted in saccade peak velocity for the test group (Hedges' g = 0.49). The results from this study suggest that the early introduction of DICOM imaging into a medical school curriculum does impact students' performance when asked to identify anatomical structures on a standardized quiz.

A multicentre study to evaluate the diagnostic performance of a novel CAD software, DecXpert, for radiological diagnosis of tuberculosis in the northern Indian population

Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed “DecXpert” a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85–0.93) and 85% specificity (95% CI 0.82–0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88–0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.

Comparison between artificial intelligence solution and radiologist for the detection of pelvic, hip and extremity fractures on radiographs in adult using CT as standard of reference

PurposeThe purpose of this study was to compare the diagnostic performance of an artificial intelligence (AI) solution for the detection of fractures of pelvic, proximal femur or extremity fractures in adults with radiologist interpretation of radiographs, using standard dose CT examination as the standard of reference. Materials and methodsThis retrospective study included 94 adult patients with suspected bone fractures who underwent a standard dose CT examination and radiographs of the pelvis and/or hip and extremities at our institution between January 2022 and August 2023. For all patients, an AI solution was used retrospectively on the radiographs to detect and localize bone fractures of the pelvis and/or hip and extremities. Results of the AI solution were compared to the reading of each radiograph by a radiologist using McNemar test. The results of standard dose CT examination as interpreted by a senior radiologist were used as the standard of reference. ResultA total of 94 patients (63 women; mean age, 56.4 ± 22.5 [standard deviation] years) were included. Forty-seven patients had at least one fracture, and a total of 71 fractures were deemed present using the standard of reference (25 hand/wrist, 16 pelvis, 30 foot/ankle). Using the standard of reference, the analysis of radiographs by the AI solution resulted in 58 true positive, 13 false negative, 33 true negative and 15 false positive findings, yielding 82 % sensitivity (58/71; 95 % confidence interval [CI]: 71–89 %), 69 % specificity (33/48; 95 % CI: 55–80 %), and 76 % accuracy (91/119; 95 % CI: 69–84 %). Using the standard of reference, the reading of the radiologist resulted in 65 true positive, 6 false negative, 42 true negative and 6 false positive findings, yielding 92 % sensitivity (65/71; 95 % CI: 82–96 %), 88 % specificity (42/48; 95 % CI: 75–94 %), and 90 % accuracy (107/119; 95 % CI: 85–95 %). The radiologist outperformed the AI solution in terms of sensitivity (P = 0.045), specificity (P = 0.016), and accuracy (P < 0.001). ConclusionIn this study, the radiologist outperformed the AI solution for the diagnosis of pelvic, hip and extremity fractures of the using radiographs. This raises the question of whether a strong standard of reference for evaluating AI solutions should be used in future studies comparing AI and human reading in fracture detection using radiographs.

Assessing GPT-4 multimodal performance in radiological image analysis.

This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology. We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images. GPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately. While the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics. Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety. GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.