Does it matter for the radiologists’ performance whether they read short or long batches in organized mammographic screening?

Backmann, Heinrich A; Larsen, Marthe; Danielsen, Anders Skyrud; Hofvind, Solveig

doi:10.1007/s00330-021-08010-9

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…Laboratory studies have demonstrated that breast images induce robust adaptation, including selective adaptation effects based on mammographic breast density (Kompaniez et al, 2013; Kompaniez-Dunigan et al, 2015, 2018; Parthasarathy et al, 2023). This is consistent with clinical observation studies showing improved reading performance over the course of a batch (Abbey et al, 2020; Backmann et al, 2021; Taylor-Phillips et al, 2016). This provides convergent evidence for adaptation as a mechanism that impacts and can potentially improve performance in batch reading of screening mammograms.…”

Section: Adaptation To Specialized Environments – the Case Of Medical...supporting

confidence: 89%

Designing for Sensory Adaptation: What You See Depends on What You’ve Been Looking at - Recommendations, Guidelines and Standards Should Reflect This

Webster,

Parthasarathy,

Zuley

et al. 2023

Policy Insights from the Behavioral and Brain Sciences

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Sensory systems continuously recalibrate their responses according to the current stimulus environment. As a result, perception is strongly affected by the current and recent context. These adaptative changes affect both sensitivity (e.g., habituating to noise, seeing better in the dark) and appearance (e.g., how things look, what catches attention) and adjust to many perceptual properties (e.g., from light level to the characteristics of someone's face). They therefore have a profound effect on most perceptual experiences, and on how well the senses work in different settings. Characterizing the properties of adaptation, how it manifests, and when it influences perception in modern environments can provide insights into the diversity of human experience. Adaptation could also be leveraged both to optimize perceptual abilities (e.g., in visual inspection tasks like radiology) and to mitigate unwanted consequences (e.g., exposure to potentially unhealthy stimulus environments).

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Section: Adaptation To Specialized Environments – the Case Of Medical...supporting

confidence: 89%

Designing for Sensory Adaptation: What You See Depends on What You’ve Been Looking at - Recommendations, Guidelines and Standards Should Reflect This

Webster,

Parthasarathy,

Zuley

et al. 2023

Policy Insights from the Behavioral and Brain Sciences

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“…The overall performance of a screening program depends on organizational factors, image quality, and performance of the radiologists. Screen-reading is a perceptual task, and the radiologists’ performance might be influenced by reading volume and reading conditions [ 4 ]. We have previously shown that 23% of screen-detected cancers have a positive assessment score by only one of the two radiologists in BreastScreen Norway [ 5 ], and that 20–30% of the screen-detected and interval cancers are classified as missed in retrospective informed review studies [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations

et al. 2022

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Objectives Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading. Methods A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists. Results Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1–5 were considered negative and 6–10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1–9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%. Conclusion Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies. Key Points • Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading • The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer

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“…It is worth noting that the cancer rate in a screening study may be only 15.5 per 1000 [ 14 ], which suggests that radiologists spend most of their time reading normal scans without suspicious lesions. On the other hand, reading quality is also related to the total number of examinations and the position of the examination in the queue [ 15 ]. Short reading batches and risk-based reading queues may help further improve radiologists’ performance.…”

Section: Introductionmentioning

confidence: 99%

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time

et al. 2022

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Objectives To investigate the feasibility of automatically identifying normal scans in ultrafast breast MRI with artificial intelligence (AI) to increase efficiency and reduce workload. Methods In this retrospective analysis, 837 breast MRI examinations performed on 438 women from April 2016 to October 2019 were included. The left and right breasts in each examination were labelled normal (without suspicious lesions) or abnormal (with suspicious lesions) based on final interpretation. Maximum intensity projection (MIP) images of each breast were then used to train a deep learning model. A high sensitivity threshold was calculated based on the detection trade - off (DET) curve on the validation set. The performance of the model was evaluated by receiver operating characteristic analysis of the independent test set. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the high sensitivity threshold were calculated. Results The independent test set consisted of 178 examinations of 149 patients (mean age, 44 years ± 14 [standard deviation]). The trained model achieved an AUC of 0.81 (95% CI: 0.75–0.88) on the independent test set. Applying a threshold of 0.25 yielded a sensitivity of 98% (95% CI: 90%; 100%), an NPV of 98% (95% CI: 89%; 100%), a workload reduction of 15.7%, and a scan time reduction of 16.6%. Conclusion This deep learning model has a high potential to help identify normal scans in ultrafast breast MRI and thereby reduce radiologists’ workload and scan time. Key Points • Deep learning in TWIST may eliminate the necessity of additional sequences for identifying normal breasts during MRI screening. • Workload and scanning time reductions of 15.7% and 16.6%, respectively, could be achieved with the cost of 1 (1 of 55) false negative prediction.

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Does it matter for the radiologists’ performance whether they read short or long batches in organized mammographic screening?

Cited by 15 publications

References 22 publications

Designing for Sensory Adaptation: What You See Depends on What You’ve Been Looking at - Recommendations, Guidelines and Standards Should Reflect This

Designing for Sensory Adaptation: What You See Depends on What You’ve Been Looking at - Recommendations, Guidelines and Standards Should Reflect This

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations

Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time

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