Identifying if participants with differing diagnostic accuracy and visual search behavior during radiologic tasks also differ in nonradiologic tasks is investigated. Four clinician groups with different radiologic experience were used: a reference expert group of five consultant radiologists, four radiology registrars, five senior house officers, and six interns. Each of the four clinician groups is known to have significantly different performance in the identification of pneumothoraces in chest x-ray. Each of the 20 participants was shown 6 nonradiologic images (3 maps and 3 sets of geometric shapes) and was asked to perform search tasks. Eye movements were recorded with a Tobii TX300 (Tobii Technology, Stockholm, Sweden) eye tracker. Four eye-tracking metrics were analyzed. Variables were compared to identify any differences among the groups. All data were compared by using nonparametric methods of analysis. The average number of targets identified in the maps did not change among groups [mean=5.8 of 6 targets (range 5.6 to 6 p=0.861)]. None of the four eye-tracking metrics investigated varied with experience in either search task (p>0.5). Despite clear differences in radiologic experience, these clinician groups showed no difference in nonradiologic search pattern behavior or skill across complex images. This is another viewpoint adding to the evidence that radiologic image interpretation is a learned skill and is task specific.

This study’s purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients’ CT images (i.e., in vivo). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient’s CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with −15 HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model. This method was applied to CT data from a previous study in which diagnostic performance of 16 readers was measured for the task of detecting subtle, hypoattenuating liver lesions (N=105), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. In vivo detectability indices were estimated and compared to the human readers’ binary 2AFC outcomes using a generalized linear mixed-effects statistical model. The results of this modeling showed that the in vivo detectability indices were strongly related to 2AFC outcomes (p<0.05). Linear comparison between human-detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlation coefficients exceeding 0.84. These results suggest the potential utility of using in vivo estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.

Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index (e′), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function. The nodule properties and segmentation algorithm were modeled in terms of a task function and a template function, respectively. The e′ values were benchmarked against experimentally acquired precision values from an anthropomorphic chest phantom across 54 acquisition protocols, 2 nodule sizes, and 2 volume segmentation softwares. e′ exhibited correlation with experimental precision across nodule sizes and acquisition protocols but dependence on segmentation software. Compared to the assessment of empirical precision, which required ∼300  h to perform the segmentation, the e′ method required ∼3  h from data collection to mathematical computation. A mathematical modeling of volume quantification provides efficient prediction of quantitative performance. It establishes a method to verify quantitative compliance and to optimize clinical protocols for chest CT volumetry.

Retinal blood vessels indicate some serious health ramifications, such as cardiovascular disease and stroke. Thanks to modern imaging technology, high-resolution images provide detailed information to help analyze retinal vascular features before symptoms associated with such conditions fully develop. Additionally, these retinal images can be used by ophthalmologists to facilitate diagnosis and the procedures of eye surgery. A fuzzy noise reduction algorithm was employed to enhance color images corrupted by Gaussian noise. The present paper proposes employing a contrast limited adaptive histogram equalization to enhance illumination and increase the contrast of retinal images captured from state-of-the-art cameras. Possessing directional properties, the multistructure elements method can lead to high-performance edge detection. Therefore, multistructure elements-based morphology operators are used to detect high-quality image ridges. Following this detection, the irrelevant ridges, which are not part of the vessel tree, were removed by morphological operators by reconstruction, attempting also to keep the thin vessels preserved. A combined method of connected components analysis (CCA) in conjunction with a thresholding approach was further used to identify the ridges that correspond to vessels. The application of CCA can yield higher efficiency when it is locally applied rather than applied on the whole image. The significance of our work lies in the way in which several methods are effectively combined and the originality of the database employed, making this work unique in the literature. Computer simulation results in wide-field retinal images with up to a 200-deg field of view are a testimony of the efficacy of the proposed approach, with an accuracy of 0.9524.

What are the costs and consequences of interruptions during diagnostic radiology? The cognitive psychology literature suggests that interruptions lead to an array of negative consequences that could hurt patient outcomes and lead to lower patient throughput. Meanwhile, observational studies have both noted a strikingly high rate of interruptions and rising number of interruptions faced by radiologists. There is some observational evidence that more interruptions could lead to worse patient outcomes: Balint et al. (2014) found that the shifts with more telephone calls received in the reading room were associated with more discrepant calls. The purpose of the current study was to use an experimental manipulation to precisely quantify the costs of two different types of interruption: telephone interruption and an interpersonal interruption. We found that the first telephone interruption led to a significant increase in time spent on the case, but there was no effect on diagnostic accuracy. Eye-tracking revealed that interruptions strongly influenced where the radiologists looked: they tended to spend more time looking at dictation screens and less on medical images immediately after interruption. Our results demonstrate that while radiologists’ eye movements are reliably influenced by interruptions, the behavioral consequences were relatively mild, suggesting effective compensatory mechanisms.

The goal of this research was to examine whether search pattern training for central line positioning on chest radiographs (CXRs) improves the ability of healthcare trainees and practitioners to identify malpositioned central venous catheters. Two sets of CXRs with central catheters were shown; half of the images contained catheters that were appropriately positioned, half that were malpositioned. Subjects were asked to: mark the tip of the catheter using the simulated radiology workstations, indicate their confidence in tip localization, and state whether the catheter was appropriately positioned or malpositioned. Subjects were also given a survey assessing their thoughts about the usefulness of search pattern training and the simulated radiology workstation. There was a significant improvement in subjects’ ability to classify a catheter as malpositioned after training, p-value = 0.03. There was no significant difference in localization of the catheter tips or in the confidence for tip localization. Subjects’ responses to the questionnaire were significantly positive for all statements, indicating that they felt search pattern training using a simulated radiology workstation had a positive impact on their education. These results suggest that our knowledge of medical image perception may be useful for developing rational educational tools for image interpretation, and that simulated radiology workstations may be a helpful means of deploying these tools.

Prior research has shown that physicians’ medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist’s visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist’s current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski–Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists’ current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about previous perceptual behavior and diagnostic decisions.