Empowering Radiographers: A Call for Integrated AI Training in University Curricula.

Background: Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence. Methods: An online survey employing Google Forms consisting of 20 questions regarding the radiographers' perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers' perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire. Results: Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR = 1.89) and 1.87 (COR = 1.87). Conclusions: This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.

Reducing ' probably benign ' assessments in normal mammograms: The role of radiologist experience.

Rationale and objectives: to investigate the relationship between radiologists' experience in reporting mammograms, their caseloads, and the classification of category '3' or 'Probably Benign' on normal mammograms. Materials and Methods: A total of 92 board-certified radiologists participated. Self-reported parameters related to experience, including age, years since qualifying as a radiologist, years of experience reading mammograms, number of mammograms read per year, and hours spent reading mammograms per week, were documented. To assess the radiologists' accuracy, "Probably Benign fractions" was calculated by dividing the number of "Probably Benign findings" given by each radiologist in the normal cases by the total number of normal cases Probably Benign fractions were correlated with various factors, such as the radiologists' experience. Results: The results of the statistical analysis revealed a significant negative correlation between radiologist experience and 'Probably Benign' fractions for normal images. Specifically, for normal cases, the number of mammograms read per year (r = -0.29, P = 0.006) and the number of mammograms read over the radiologist's lifetime (r = -0.21, P = 0.049) were both negatively correlated with 'Probably Benign' fractions. Conclusion: The results indicate that a relationship exists between increased reading volumes and reduced assessments of 'Probably Benign' in normal mammograms. The implications of these findings extend to the effectiveness of screening programs and the recall rates.

Career caseload predicts interobserver agreement on the final classification of a mammogram.

INTRODUCTION: Differences in radiologists' experience can potentially introduce interobserver variability in reading mammograms. This work investigated the effect of radiologists' experience on agreement on mammographic final classification. METHODS: This was a cross-sectional study. Seventeen radiologists were asked to provide their final impression on 60 mammogram cases. Experience parameters included breast subspecialty, years reading mammograms, cases read per year and career caseload. Career caseload was calculated by multiplying years reading mammograms by the average number of cases read per year. The interobserver agreement was calculated using Cohen kappa (κ). The difference in κ between radiologists' groups was compared using the independent-sample t-test and analysis of variance. RESULTS: The average interobserver agreement was 0.25 (fair). A small difference was found in favour of breast radiologists against general radiologists (κ = 0.21 and 0.29, respectively, P = 0.019). Years reading mammograms and cases read per year did not seem to significantly affect the interobserver agreement (P = 0.056 and 0.273 respectively). Radiologist who had career caseload of at least 2500 cases showed significantly higher consistency than those who read less. κ for radiologists who had career caseload of 2500-4000 cases and >4000 cases was 0.33 and 0.28, respectively, whereas for <2500 κ was 0.17 (P = 0.001). CONCLUSION: A fair level of interobserver agreement on the final classification of a mammogram was demonstrated. Career caseload was the most important experience parameter to associate with the interobserver agreement. Training strategies aiming to increase radiologists' career caseload may be beneficial.

Compliance With Radiation Protection Practices Among Radiologists.

BACKGROUND: Radiologists are at higher risk of adverse health effects due to their occupational radiation exposure; therefore, applying protection techniques is imperative. Studies on radiologists' compliance in this regard are scarce. We aimed to assess compliance with radiation safety practices among radiologists. METHODS: Questionnaires were distributed to radiologists in tertiary hospitals. The questionnaire was designed to assess compliance in three domains: using personal protective devices, using exposure-reduction techniques during fluoroscopic exposures, and using personal dose-monitoring devices. Descriptive analysis of the compliance was performed. RESULTS: Sixty-two radiologists were included in the analysis. Use of leaded aprons and thyroid shields was commonplace, whereas only 3.2% ever use leaded eyeglasses. About half of the radiologists always considered reducing the time of exposure and avoided exposure by the primary beam, and the other half did that sometimes. Most of the radiologists (66.1%) always complied with reducing the number of unnecessary exposures, and the rest only complied sometimes. Most of the radiologists (93.5%) always used single personal dose-monitoring devices, most commonly at the neck level over the collar. There was no difference in compliance between different sexes, position descriptions, hospital types, hospital sizes, or years of experience. CONCLUSION: Future compliance improvement strategies for radiologists should focus on use of thyroid shields and leaded eyeglasses and use of exposure-reduction techniques during fluoroscopic operations.

Classification of normal screening mammograms is strongly influenced by perceived mammographic breast density.

INTRODUCTION: To investigate how breast screen readers classify normal screening cases using descriptors of normal mammographic features and to assess test cases for suitability for a single reading strategy. METHODS: Fifteen breast screen readers interpreted a test set of 29 normal screening cases and classified them by firstly rating their perceived difficulty to reach a 'normal' decision, secondly identifying the cases' salient normal mammographic features and thirdly assessing the cases' suitability for a single reading strategy. RESULTS: The relationship between the perceived difficulty in making 'normal' decisions and the normal mammographic features was investigated. Regular ductal pattern (Tb  = -0.439, P = 0.001), uniform density (Tb  = -0.527, P < 0.001), non-dense breasts (Tb  = -0.736, P < 0.001), symmetrical mammographic features (Tb  = -0.474, P = 0.001) and overlapped density (Tb  = 0.630, P < 0.001) had a moderate to strong correlation with the difficulty to make 'normal' decisions. Cases with regular ductal pattern (Tb  = 0.447, P = 0.002), uniform density (Tb  = 0.550, P < 0.001), non-dense breasts (Tb  = 0.748, P < 0.001) and symmetrical mammographic features (Tb  = 0.460, P = 0.001) were considered to be more suitable for single reading, whereas cases with overlapped density were not (Tb  = -0.679, P < 0.001). CONCLUSION: The findings suggest that perceived mammographic breast density has a major influence on the difficulty for readers to classify cases as normal and hence their suitability for single reading.

Impact of Breast Reader Assessment Strategy on mammographic radiologists' test reading performance.

INTRODUCTION: The detection of breast cancer is somewhat limited by human factors, and thus there is a need to improve reader performance. This study assesses whether radiologists who regularly undertake the education in the form of the Breast Reader Assessment Strategy (BREAST) demonstrate any changes in mammography interpretation performance over time. METHODS: In 2011, 2012 and 2013, 14 radiologists independently assessed a year-specific BREAST mammographic test-set. Radiologists read a different single test-set once each year, with each comprising 60 digital mammogram cases. Radiologists marked the location of suspected lesions without computer-aided diagnosis (CAD) and assigned a confidence rating of 2 for benign and 3-5 for malignant lesions. The mean sensitivity, specificity, location sensitivity, JAFROC FOM and ROC AUC were calculated. A Kruskal-Wallis test was used to compare the readings for the 14 radiologists across the 3 years. Wilcoxon signed rank test was used to assess comparison between pairs of years. Relationships between changes in performance and radiologist characteristics were examined using a Spearman's test. RESULTS: Significant increases were noted in mean sensitivity (P = 0.01), specificity (P = 0.01), location sensitivity (P = 0.001) and JAFROC FOM (P = 0.001) between 2011 and 2012. Between 2012 and 2013, significant improvements were noted in mean sensitivity (P = 0.003), specificity (P = 0.002), location sensitivity (P = 0.02), JAFROC FOM (P = 0.005) and ROC AUC (P = 0.008). No statistically significant correlations were shown between the levels of improvement and radiologists' characteristics. CONCLUSION: Radiologists' who undertake the BREAST programme demonstrate significant improvements in test-set performance during a 3-year period, highlighting the value of ongoing education through the use of test-set.

Markers of good performance in mammography depend on number of annual readings.

PURPOSE: To explore relationships between reader performance and reader characteristics in mammography for specific radiologist groupings on the basis of annual number of readings. MATERIALS AND METHODS: The institutional review board approved the study and waived the need for patient consent to use all images. Readers gave informed consent. One hundred sixteen radiologists independently reviewed 60 mammographic cases: 20 cases with cancer and 40 cases with normal findings. Readers located any visualized cancer, and levels of confidence were scored from 1 to 5. A jackknifing free response operating characteristic (JAFROC) method was used, and figures of merit along with sensitivity and specificity were correlated with reader characteristics by using Spearman techniques and standard multiple regressions. RESULTS: Reader performance was positively correlated with number of years since qualification as a radiologist (P ≤ .01), number of years reading mammograms (P ≤ .03), and number of readings per year (P ≤ .0001). The number of years since qualification as a radiologist (P ≤ .004) and number of years of reading mammograms (P ≤ .002) were negatively related to JAFROC values for radiologists with annual volumes of less than 1000 mammographic readings. For individuals with more than 5000 mammographic readings per year, JAFROC values were positively related to the number of years that the reader was qualified as a radiologist (P ≤ .01), number of years of reading mammograms (P ≤ .002), and number of hours per week of reading mammograms (P ≤ .003). Number of mammographic readings per year was positively related with JAFROC scores for readers with an annual volume between 1000 and 5000 readings (P ≤ .03). Differences in JAFROC scores appear to be more related to specificity than location sensitivity, with the former demonstrating significant relationships with four of the five characteristics analyzed, whereas no relationships were shown for the latter. CONCLUSION: Radiologists' determinants of performance are associated with annual reading volumes. Ability to recognize normal images is a discriminating factor in individuals with a high volume of mammographic readings.

Quantitative measures confirm the inverse relationship between lesion spiculation and detection of breast masses.

OBJECTIVE: To identify specific mammographic appearances that reduce the mammographic detection of breast cancer. MATERIALS AND METHODS: This study received institutional board review approval and all readers gave informed consent. A set of 60 mammograms each consisting of craniocaudal and mediolateral oblique projections were presented to 129 mammogram Breastscreen readers. The images consisted of 20 positive cases with single and multicentric masses in 16 and 4 cases, respectively (resulting in a total of 24 cancers), and readers were asked to identify and locate the lesions. Each lesion was then ranked according to a detectability rating (ie, the number of observers who correctly located the lesion divided by the total number of observers), and this was correlated with breast density, lesion size, and various descriptors of lesion shape and texture. RESULTS: Negative and positive correlations between lesion detection and density (r = -0.64, P = .007) and size (r = 0.65, P = .005), respectively, were demonstrated. In terms of lesion size and shape, there were significant correlations between the probability of detection and area (r = 0.43, P = .04), perimeter (r = 0.66, P = .0004), lesion elongation (r = 0.49, P = .02), and lesion nonspiculation (r = 0.78, P < .0001). CONCLUSIONS: The results of this study have identified specific lesion characteristics associated with shape that may contribute to reduced cancer detection. Mammographic sensitivity may be adversely affected without appropriate attention to spiculation.