In vitro blood sample assessment: investigating correlation of laboratory hemoglobin and spectral properties of dual-energy CT measurements (ρ/Z).

OBJECTIVES: Our study comprised a single-center retrospective in vitro correlation between spectral properties, namely ρ/Z values, derived from scanning blood samples using dual-energy computed tomography (DECT) with the corresponding laboratory hemoglobin/hematocrit (Hb/Hct) levels and assessed the potential in anemia-detection. METHODS: DECT of 813 patient blood samples from 465 women and 348 men was conducted using a standardized scan protocol. Electron density relative to water (ρ or rho), effective atomic number (Zeff), and CT attenuation (Hounsfield unit) were measured. RESULTS: Positive correlation with the Hb/Hct was shown for ρ (r-values 0.37-0.49) and attenuation (r-values 0.59-0.83) while no correlation was observed for Zeff (r-values -0.04 to 0.08). Significant differences in attenuation and ρ values were detected for blood samples with and without anemia in both genders (p value < 0.001) with area under the curve ranging from 0.7 to 0.95. Depending on the respective CT parameters, various cutoff values for CT-based anemia detection could be determined. CONCLUSION: In summary, our study investigated the correlation between DECT measurements and Hb/Hct levels, emphasizing novel aspects of ρ and Zeff values. Assuming that quantitative changes in the number of hemoglobin proteins might alter the mean Zeff values, the results of our study show that there is no measurable correlation on the atomic level using DECT. We established a positive in vitro correlation between Hb/Hct values and ρ. Nevertheless, attenuation emerged as the most strongly correlated parameter with identifiable cutoff values, highlighting its preference for CT-based anemia detection. CLINICAL RELEVANCE STATEMENT: By scanning multiple blood samples with dual-energy CT scans and comparing the measurements with standard laboratory blood tests, we were able to underscore the potential of CT-based anemia detection and its advantages in clinical practice. KEY POINTS: Prior in vivo studies have found a correlation between aortic blood pool and measured hemoglobin and hematocrit. Hemoglobin and hematocrit correlated with electron density relative to water and attenuation but not Zeff. Dual-energy CT has the potential for additional clinical benefits, such as CT-based anemia detection.

DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.

OBJECTIVES: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

Three scans are better than two for follow-up: An automatic method for finding missed and misidentified lesions in cross-sectional follow-up of oncology patients.

PURPOSE: Missed and misidentified neoplastic lesions in longitudinal studies of oncology patients are pervasive and may affect the evaluation of the disease status. Two newly identified patterns of lesion changes, lone lesions and non-consecutive lesion changes, may help radiologists to detect these lesions. This study evaluated a new interpretation revision workflow of lesion annotations in three or more consecutive scans based on these suspicious patterns. METHODS: The interpretation revision workflow was evaluated on manual and computed lesion annotations in longitudinal oncology patient studies. For the manual revision, a senior radiologist and a senior neurosurgeon (the readers) manually annotated the lesions in each scan and later revised their annotations to identify missed and misidentified lesions with the workflow using the automatically detected patterns. For the computerized revision, lesion annotations were first computed with a previously trained nnU-Net and were then automatically revised with an AI-based method that automates the workflow readers' decisions. The evaluation included 67 patient studies with 2295 metastatic lesions in lung (19 patients, 83 CT scans, 1178 lesions), liver (18 patients, 77 CECT scans, 800 lesions) and brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions). RESULTS: Revision of the manual lesion annotations revealed 120 missed lesions and 20 misidentified lesions in 31 out of 67 (46%) studies. The automatic revision reduced the number of computed missed lesions by 55 and computed misidentified lesions by 164 in 51 out of 67 (76%) studies. CONCLUSION: Automatic analysis of three or more consecutive volumetric scans helps find missed and misidentified lesions and may improve the evaluation of temporal changes of oncological lesions.

Effectiveness of using concept mapping by dental students in the radiographic interpretation of jaw diseases.

OBJECTIVES: Interpretation of dental radiographs is a difficult process, particularly for inexperienced students. This study introduced concept mapping for dental students to help in the radiographic interpretation of common jaw lesions. We aimed to analyze the efficacy of the concept map (CM) in radiographic interpretation, with a discussion of the diagnostic reasoning dilemma. METHODS: This study included 39 dental students. After a 1-h class for CM guidance and based on three group discussions and one-on-one feedback, the students completed and submitted CMs for three jaw diseases (ameloblastoma, odontogenic keratocyst, and simple bone cyst). All participants underwent a pretest and posttest of knowledge and diagnosis; all students but one completed an open-ended questionnaire regarding the use of CMs. RESULTS: Concept mapping effectively improved diagnostic accuracy. The participants' posttest scores were better than their pretest scores in both knowledge and diagnostic tests. Most of the students attempted radiographic interpretation through analytic reasoning. The time required for the students to draw a CM varied from student to student from 3-5 h to 1-3 days. CONCLUSION: This study shows that CMs can improve the radiographic diagnostic ability of dental students by providing a framework for analytic reasoning. Continuous research is warranted to improve the effectiveness of CM in oral radiographic interpretation in the dental student's class.

The application of lasers in vital pulp therapy: clinical and radiographic outcomes.

The main purpose of vital pulp therapy (VPT) is to preserve the integrity and function of the pulp. A wide variety of materials and techniques have been proposed to improve treatment outcomes, and among them, the utilization of lasers has gained significant attention. The application of lasers in different stages of VPT has witnessed remarkable growth in recent years, surpassing previous approaches.This study aimed to review the applications of lasers in different steps of VPT and evaluate associated clinical and radiographic outcomes. An electronic search using Scopus, MEDLINE, Web of Science and Google Scholar databases from 2000 to 2023 was carried out by two independent researchers. The focus was on human studies that examined the clinical and/or radiographic effects of different laser types in VPT. A total of 4243 studies were included in this narrative review article. Based on the compiled data, it can be concluded that although current literature suggests laser may be proposed as an adjunct modality for some procedural steps in VPT, more research with standardized methodologies and criteria is needed to obtain more reliable and conclusive results.

Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.

OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network. METHODS: This retrospective study included consecutive patients with colorectal adenocarcinoma who underwent abdominal CT before CRC resection surgery (training set = 379, test set = 103). We customized the 3D U-Net of nnU-Net (CUNET) for CRC detection, which was trained with fivefold cross-validation using annotated CT images. CUNET was validated using datasets covering various clinical situations and institutions: an internal test set (n = 103), internal patients with CRC first determined by CT (n = 54) and asymptomatic CRC (n = 51), and an external validation set from two institutions (n = 60). During each validation, data from the healthy population were added (internal = 60; external = 130). CUNET was compared with other deep CNNs: residual U-Net and EfficientDet. The CAD performances were evaluated using per-CRC sensitivity (true positive/all CRCs), free-response receiver operating characteristic (FROC), and jackknife alternative FROC (JAFROC) curves. RESULTS: CUNET showed a higher maximum per-CRC sensitivity than residual U-Net and EfficientDet (internal test set 91.3% vs. 61.2%, and 64.1%). The per-CRC sensitivity of CUNET at false-positive rates of 3.0 was as follows: internal CRC determined by CT, 89.3%; internal asymptomatic CRC, 87.3%; and external validation, 89.6%. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 89.7% (252/281) of CRCs from all validation sets. CONCLUSIONS: CUNET can detect CRC on abdominal CT in patients with various clinical situations and from external institutions. KEY POINTS: • Customized 3D U-Net of nnU-Net (CUNET) can be applied to the opportunistic detection of colorectal cancer (CRC) in abdominal CT, helping radiologists detect unexpected CRC. • CUNET showed the best performance at false-positive rates ≥ 3.0, and 30.1% of false-positives were in the colorectum. CUNET detected 69.2% (9/13) of CRCs missed by radiologists and 87.3% (48/55) of asymptomatic CRCs. • CUNET detected CRCs in multiple validation sets composed of varying clinical situations and from different institutions, and CUNET detected 89.7% (252/281) of CRCs from all validation sets.

Automatic Segmentation and Radiologic Measurement of Distal Radius Fractures Using Deep Learning.

Background: Recently, deep learning techniques have been used in medical imaging studies. We present an algorithm that measures radiologic parameters of distal radius fractures using a deep learning technique and compares the predicted parameters with those measured by an orthopedic hand surgeon. Methods: We collected anteroposterior (AP) and lateral X-ray images of 634 wrists in 624 patients with distal radius fractures treated conservatively with a follow-up of at least 2 months. We allocated 507 AP and 507 lateral images to the training set (80% of the images were used to train the model, and 20% were utilized for validation) and 127 AP and 127 lateral images to the test set. The margins of the radius and ulna were annotated for ground truth, and the scaphoid in the lateral views was annotated in the box configuration to determine the volar side of the images. Radius segmentation was performed using attention U-Net, and the volar/dorsal side was identified using a detection and classification model based on RetinaNet. The proposed algorithm measures the radial inclination, dorsal or volar tilt, and radial height by index axes and points from the segmented radius and ulna. Results: The segmentation model for the radius exhibited an accuracy of 99.98% and a Dice similarity coefficient (DSC) of 98.07% for AP images, and an accuracy of 99.75% and a DSC of 94.84% for lateral images. The segmentation model for the ulna showed an accuracy of 99.84% and a DSC of 96.48%. Based on the comparison of the radial inclinations measured by the algorithm and the manual method, the Pearson correlation coefficient was 0.952, and the intraclass correlation coefficient was 0.975. For dorsal/volar tilt, the correlation coefficient was 0.940, and the intraclass correlation coefficient was 0.968. For radial height, it was 0.768 and 0.868, respectively. Conclusions: The deep learning-based algorithm demonstrated excellent segmentation of the distal radius and ulna in AP and lateral radiographs of the wrist with distal radius fractures and afforded automatic measurements of radiologic parameters.

Pedicle Screw Pseudofracture on Computed Tomography Secondary to Metal Artifact Reduction.

Metal artifact reduction (MAR) algorithms are commonly used in computed tomography (CT) scans where metal implants are involved. However, MAR algorithms also have the potential to create new artifacts in reconstructed images. We present a case of a screw pseudofracture due to MAR on CT.

Formalising written preliminary image evaluation by Australian radiographers: a review of practice value.

The Medical Radiation Practice Board of Australia (MRPBA) minimum competency framework requires all Australian radiographers to identify significant pathology in radiological images and take appropriate action to alert these urgent findings and ensure patient safety. Despite professional bodies endorsing the provision of preliminary image evaluations (PIE) in written format, radiographer image interpretation often remains inconsistent, informal, or undocumented. The purpose of this narrative review was to assess the literature to determine if PIE in the form of written radiographer comments is of value to the Australian healthcare system. A structured search was completed using four health research databases: CINAHL, Medline, Scopus and Web of Science. Studies have suggested that there is a contextual need for commenting due to increased imaging service pressures, radiologist shortages and subsequent reporting delays. Radiographers appear well placed and willing to provide accurate initial input with evidence that this would be valued and appreciated within the multidisciplinary team. Radiographer commenting has also been shown to reduce diagnostic and communicative errors with the potential to improve patient management. Finally, it was shown that participation in image interpretation practices can enhance recruitment, retention and job satisfaction among radiographers. Therefore, the current literature supports implementation of radiographer commenting within the Australian healthcare system.

CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study.

PURPOSE: Peritoneal carcinomatosis (PC) and peritoneal tuberculosis (PTB) have similar clinical and radiologic imaging features, which make it very difficult to differentiate between the two entities clinically. Our aim was to determine if the CT textural parameters of omental lesions among patients with PC were different from those with PTB. METHODS: All patients who had undergone omental biopsy at our institution from January 2010 to December 2018 and had a tissue diagnosis of PC or PTB were eligible for inclusion. Patients who did not have a contrast-enhanced CT abdomen within one month of the omental biopsy were excluded. A region of interest (ROI) was manually drawn over omental lesions and radiomic features were extracted using open-source LIFEx software. Statistical analysis was performed to compare mean differences in CT texture parameters between the PC and PTB groups. RESULTS: A total of 66 patients were included in the study of which 38 and 28 had PC and PTB, respectively. Omental lesions in patients with PC had higher mean radiodensity (mean difference: +32.4; p = 0.001), higher mean entropy (mean difference: +0.11; p < 0.001), and lower mean energy (mean difference: -0.024; p = 0.001) compared to those in PTB. Additionally, omental lesions in the PC group had lower gray-level co-occurrence matrix (GLCM) homogeneity (mean difference: -0.073; p < 0.001) and higher GLCM dissimilarity (mean difference: +0.480; p < 0.001) as compared to the PTB group. CONCLUSION: CT texture parameters of omental lesions differed significantly between patients with PTB and those with PC, which may help clinicians in differentiating between the two entities.

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.

BACKGROUND: Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios. OBJECTIVE: This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization. METHODS: This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining. RESULTS: The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D. CONCLUSIONS: Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.

Diagnostic accuracy of cone-beam computed tomography with modified grayscale range for detection of buccal cortical plate defects adjacent to dental implants.

Background: This study assessed the diagnostic accuracy of cone-beam computed tomography (CBCT) with a modified grayscale range for the detection of buccal cortical plate defects adjacent to dental implants. Materials and Methods: In this in vitro experimental study, titanium implants were inserted in 168 fresh bovine bone blocks with 1-1.5 mm of buccal cortical plate thickness. The blocks were randomly divided into four groups (n = 42). No defect was created in the control blocks. In the three experimental groups, cortical plate defects were randomly created in the cervical, middle, or apical third by a round bur with a 2-mm diameter (n = 42). All blocks underwent CBCT with and without change in the grayscale range. Two observers evaluated all images regarding the presence/absence of defects. Kappa test is used for the agreement of the observers. The diagnostic accuracy of the two modalities was compared by calculating the area under the receiver operating characteristic curve (AUC) (P ≤ 0.05). The sensitivity and specificity values were also compared. Results: The AUC was not significantly different between the two modalities with and without altered grayscale range (0.754 vs. 0.762, respectively, P = 0.716). The diagnostic sensitivity of CBCT with and without change in the grayscale range was 51% and 52%, respectively, with a specificity of 100% for both. The diagnostic accuracy of CBCT with and without altered grayscale range had no significant difference for apical and middle third defects (P > 0.05) and was significantly higher than that for the cervical third defects (P < 0.05). Conclusion: Changing the grayscale range does not improve the diagnostic accuracy of CBCT for the detection of buccal cortical plate defects adjacent to dental implants.

Development and evaluation of an integrated model based on a deep segmentation network and demography-added radiomics algorithm for segmentation and diagnosis of early lung adenocarcinoma.

Non-invasive early detection and differentiation grading of lung adenocarcinoma using computed tomography (CT) images are clinically important for both clinicians and patients, including determining the extent of lung resection. However, these are difficult to accomplish using preoperative images, with CT-based diagnoses often being different from postoperative pathologic diagnoses. In this study, we proposed an integrated detection and classification algorithm (IDCal) for diagnosing ground-glass opacity nodules (GGN) using CT images and other patient informatics, and compared its performance with that of other diagnostic modalities. All labeling was confirmed by a thoracic surgeon by referring to the patient's CT image and biopsy report. The detection phase was implemented via a modified FC-DenseNet to contour the lesions as elaborately as possible and secure the reliability of the classification phase for subsequent applications. Then, by integrating radiomics features and other patients' general information, the lesions were dichotomously reclassified into "non-invasive" (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and "invasive" (invasive adenocarcinoma). Data from 168 GGN cases were used to develop the IDCal, which was then validated in 31 independent CT scans. IDCal showed a high accuracy of GGN detection (sensitivity, 0.970; false discovery rate, 0.697) and classification (accuracy, 0.97; f1-score, 0.98; ROAUC, 0.96). In conclusion, the proposed IDCal detects and classifies GGN with excellent performance. Thus, it can be suggested that our multimodal prediction model has high potential as an auxiliary diagnostic tool of GGN to help clinicians.

Three dimensional computed tomography texture analysis of pulmonary lesions: Does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia?

PURPOSE: To investigate whether CT texture analysis allows differentiation between adenocarcinomas, squamous cell carcinomas, carcinoids, small cell lung cancers and organizing pneumonia and between carcinomas and neuroendocrine tumors. METHOD: This retrospective study included patients 133 patients (30 patients with organizing pneumonia, 30 patients with adenocarcinoma, 30 patients with squamous cell carcinoma, 23 patients with small cell lung cancer, 20 patients with carcinoid), who underwent CT-guided biopsy of the lung and had a corresponding histopathologic diagnosis. Pulmonary lesions were segmented in consensus by two radiologists with and without a threshold of -50HU in three dimensions. Groupwise comparisons were performed to assess for differences between all five above-listed entities and between carcinomas and neuroendocrine tumors. RESULTS: Pairwise comparisons of the five entities revealed 53 statistically significant texture features when using no HU-threshold and 6 statistically significant features with a threshold of -50HU. The largest AUC (0.818 [95%CI 0.706-0.930]) was found for the feature wavelet-HHH_glszm_SmallAreaEmphasis for discrimination of carcinoid from the other entities when using no HU-threshold. In differentiating neuroendocrine tumors from carcinomas, 173 parameters proved statistically significant when using no HU threshold versus 52 parameters when using a -50HU-threshold. The largest AUC (0.810 [95%CI 0.728-0,893]) was found for the parameter original_glcm_Correlation for discrimination of neuroendocrine tumors from carcinomas when using no HU-threshold. CONCLUSIONS: CT texture analysis revealed features that differed significantly between malignant pulmonary lesions and organizing pneumonia and between carcinomas and neuroendocrine tumors of the lung. Applying a HU-threshold for segmentation substantially influenced the results of texture analysis.

Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs.

Recent studies of automatic diagnosis of vertebral compression fractures (VCFs) using deep learning mainly focus on segmentation and vertebral level detection in lumbar spine lateral radiographs (LSLRs). Herein, we developed a model for simultaneous VCF diagnosis and vertebral level detection without using adjacent vertebral bodies. In total, 1102 patients with VCF, 1171 controls were enrolled. The 1865, 208, and 198 LSLRS were divided into training, validation, and test dataset. A ground truth label with a 4-point trapezoidal shape was made based on radiological reports showing normal or VCF at some vertebral level. We applied a modified U-Net architecture, in which decoders were trained to detect VCF and vertebral levels, sharing the same encoder. The multi-task model was significantly better than the single-task model in sensitivity and area under the receiver operating characteristic curve. In the internal dataset, the accuracy, sensitivity, and specificity of fracture detection per patient or vertebral body were 0.929, 0.944, and 0.917 or 0.947, 0.628, and 0.977, respectively. In external validation, those of fracture detection per patient or vertebral body were 0.713, 0.979, and 0.447 or 0.828, 0.936, and 0.820, respectively. The success rates were 96 % and 94 % for vertebral level detection in internal and external validation, respectively. The multi-task-shared encoder was significantly better than the single-task encoder. Furthermore, both fracture and vertebral level detection was good in internal and external validation. Our deep learning model may help radiologists perform real-life medical examinations.

Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study.

PURPOSE: Detection of hepatocellular carcinoma (HCC) is crucial during surveillance by ultrasound. We previously developed an artificial intelligence (AI) system based on convolutional neural network for detection of focal liver lesions (FLLs) in ultrasound. The primary aim of this study was to evaluate whether the AI system can assist non-expert operators to detect FLLs in real-time, during ultrasound examinations. METHOD: This single-center prospective randomized controlled study evaluated the AI system in assisting non-expert and expert operators. Patients with and without FLLs were enrolled and had ultrasound performed twice, with and without AI assistance. McNemar's test was used to compare paired FLL detection rates and false positives between groups with and without AI assistance. RESULTS: 260 patients with 271 FLLs and 244 patients with 240 FLLs were enrolled into the groups of non-expert and expert operators, respectively. In non-experts, FLL detection rate in the AI assistance group was significantly higher than the no AI assistance group (36.9 % vs 21.4 %, p < 0.001). In experts, FLL detection rates were not significantly different between the groups with and without AI assistance (66.7 % vs 63.3 %, p = 0.32). False positive detection rates in the groups with and without AI assistance were not significantly different in both non-experts (14.2 % vs 9.2 %, p = 0.08) and experts (8.6 % vs 9.0 %, p = 0.85). CONCLUSIONS: The AI system resulted in significant increase in detection of FLLs during ultrasound examinations by non-experts. Our findings may support future use of the AI system in resource-limited settings where ultrasound examinations are performed by non-experts. The study protocol was registered under the Thai Clinical Trial Registry (TCTR20201230003), which is part of the WHO ICTRP Registry Network. The registry can be accessed via the following URL: https://trialsearch.who.int/Trial2.aspx?TrialID=TCTR20201230003.

Radiologists' perceptions of knowledge and training required by radiographers in the interpretation of radiographic images: An explorative study in KwaZulu-Natal province, South Africa.

INTRODUCTION: The health sector of South Africa is burdened by the shortage of radiologists leading to the under-reporting of radiographic images and ultimately mismanagement of patients. Previous studies have recommended training of radiographers in radiographic image interpretation in order to improve the reporting. There is paucity of information regarding the knowledge and training required by radiographers to interpret radiographic images. The purpose of this study was therefore to explore the knowledge and training required by diagnostic radiographers, according to radiologists, for the interpretation of radiographs. METHOD: A qualitative descriptive study employing criterion sampling to select qualified radiologists practicing in the eThekwini district of the KwaZulu Natal province, was conducted. One-on-one and in-depth, semi-structured interviews were used to collect data from three participants. The interviews were not carried out face to face as a result of the Covid 19 pandemic and the regulation of social distancing. This did not permit engagement with research communities. The data from the interviews were analysed using Tesch's eight steps for analysing qualitative data. RESULTS: Findings revealed that radiologists supported the interpretation of radiographic images by radiographers in rural settings, and for the radiographer's scope of practice to be restructured to include the reporting of chest and the musculoskeletal system images. The themes that emerged from the analysis included knowledge, training, clinical competencies and medico-legal responsibilities required by radiographers in the interpretation of radiographic images. CONCLUSION: Although the radiologists support the training of radiographers in the interpretation of radiographic images, radiologists think that the scope of practice should be limited to the interpretation of the chest and musculoskeletal systems in rural areas only.

Achieving Exceptional Cochlea Delineation in Radiotherapy Scans: The Impact of Optimal Window Width and Level Settings.

Introduction Radiation therapy (RT) aims to maximize the dose to the target volume while minimizing the dose to organs at risk (OAR), which is crucial for optimal treatment outcomes and minimal side effects. The complex anatomy of the head and neck regions, including the cochlea, presents challenges in radiotherapy. Accurate delineation of the cochlea is essential to prevent toxicities such as sensorineural hearing loss. Educational interventions, including seminars, atlases, and multidisciplinary discussions, can improve accuracy and interobserver agreement in contouring. This study seeks to provide radiation oncology practitioners with the necessary window width and window level settings in computed tomography (CT) scans to accurately and precisely delineate the cochlea, using a pre-and post-learning phase approach to assess the change in accuracy. Methods and materials The study used the ProKnow Contouring Accuracy Program (ProKnow, LLC, Florida, United States), which employs the StructSure method and the Dice coefficient to assess the precision of a user's contour compared to an expert contour. The StructSure method offers superior sensitivity and accuracy, while the Dice coefficient is a more rudimentary and less sensitive approach. Two datasets of CT scans, one for each left and right cochlea, were used. The author delineated the cochlea before and after applying the proposed technique for window width and window level, comparing the results with those of the expert and general population. The study included a step-by-step method for cochlea delineation using window width and window level settings. Data analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States). Results The implementation of the proposed step-by-step method for adjusting window width and window level led to significant improvements in contouring accuracy and delineation quality in radiation therapy planning. Comparing pre- and post-intervention scenarios, the author exhibited increased StructSure scores (right cochlea: 88.81 to 99.15; left cochlea: 88.45 to 99.85) and Dice coefficient scores (right cochlea: 0.62 to 0.80; left cochlea: 0.73 to 0.86). The author consistently demonstrated higher contouring accuracy and greater similarity to expert contours compared to the group's mean scores both before and after the intervention. These results suggest that the proposed method enhances the precision of cochlea delineation in radiotherapy planning. Conclusion In conclusion, this study demonstrated that a step-by-step instructional approach for adjusting window width and window level significantly improved cochlea delineation accuracy in radiotherapy contouring. The findings hold potential clinical implications for reducing radiation-related side effects and improving patient outcomes. This study supports the integration of the instructional technique into radiation oncology training and encourages further exploration of advanced imaging processing and artificial intelligence applications in radiotherapy contouring.

Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set.

This study aimed to evaluate the efficacy of deep learning (DL) for the identification and classification of various types of dental implant systems (DISs) using a large-scale multicenter data set. We also compared the classification accuracy of DL and dental professionals. The data set, which was collected from 5 college dental hospitals and 10 private dental clinics, contained 37,442 (24.8%) periapical and 113,291 (75.2%) panoramic radiographic images and consisted of a total of 10 manufacturers and 25 different types of DISs. The classification accuracy of DL was evaluated using a pretrained and modified ResNet-50 architecture, and comparison of accuracy performance and reading time between DL and dental professionals was conducted using a self-reported questionnaire. When comparing the accuracy performance for classification of DISs, DL (accuracy: 82.0%; 95% confidence interval [CI], 75.9%-87.0%) outperformed most of the participants (mean accuracy: 23.5% ± 18.5%; 95% CI, 18.5%-32.3%), including dentists specialized (mean accuracy: 43.3% ± 20.4%; 95% CI, 12.7%-56.2%) and not specialized (mean accuracy: 16.8% ± 9.0%; 95% CI, 12.8%-20.9%) in implantology. In addition, DL tends to require lesser reading and classification time (4.5 min) than dentists who specialized (75.6 ± 31.0 min; 95% CI, 13.1-78.4) and did not specialize (91.3 ± 38.3 min; 95% CI, 74.1-108.6) in implantology. DL achieved reliable outcomes in the identification and classification of various types of DISs, and the classification accuracy performance of DL was significantly superior to that of specialized or nonspecialized dental professionals. DL as a decision support aid can be successfully used for the identification and classification of DISs encountered in clinical practice.

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging.

BACKGROUND: Deep learning (DL) algorithms are playing an increasing role in automatic medical image analysis. PURPOSE: To evaluate the performance of a DL model for the automatic detection of intracranial haemorrhage and its subtypes on non-contrast CT (NCCT) head studies and to compare the effects of various preprocessing and model design implementations. METHODS: The DL algorithm was trained and externally validated on open-source, multi-centre retrospective data containing radiologist-annotated NCCT head studies. The training dataset was sourced from four research institutions across Canada, the USA and Brazil. The test dataset was sourced from a research centre in India. A convolutional neural network (CNN) was used, with its performance compared against similar models with additional implementations: (1) a recurrent neural network (RNN) attached to the CNN, (2) preprocessed CT image-windowed inputs and (3) preprocessed CT image-concatenated inputs. The area under the receiver operating characteristic curve (AUC-ROC) and microaveraged precision (mAP) score were used to evaluate and compare model performances. RESULTS: The training and test datasets contained 21,744 and 491 NCCT head studies, respectively, with 8,882 (40.8%) and 205 (41.8%) positive for intracranial haemorrhage. Implementation of preprocessing techniques and the CNN-RNN framework increased mAP from 0.77 to 0.93 and increased AUC-ROC [95% confidence intervals] from 0.854 [0.816-0.889] to 0.966 [0.951-0.980] (p-value = 3.91 × 10-12). CONCLUSIONS: The deep learning model accurately detected intracranial haemorrhage and improved in performance following specific implementation techniques, demonstrating clinical potential as a decision support tool and an automated system to improve radiologist workflow efficiency. KEY POINTS: • The deep learning model detected intracranial haemorrhages on computed tomography with high accuracy. • Image preprocessing, such as windowing, plays a large role in improving deep learning model performance. • Implementations which enable an analysis of interslice dependencies can improve deep learning model performance. • Visual saliency maps can facilitate explainable artificial intelligence systems. • Deep learning within a triage system may expedite earlier intracranial haemorrhage detection.