CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography

Mang, Thomas; Bogoni, Luca; Anand, Vikram; Dass, C.; Curtin, Andrew; Lev-Toaff, Anna S.; Hermosillo, Gerardo; Noah, Ralph; Raykar, Vikas C.; Salganicoff, Marcos; Shaw, Robert D.; Summerton, Susan; Tappouni, Rafel; Ringel, H.; Weber, Michael; Wolf, Matthias; Obuchowski, Nancy A.

doi:10.1007/s00330-014-3158-1

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…We assume that second-read CAD would increase sensitivity for patients with any polyp by approximately 16.6% while decreasing specificity by approximately -5.5%. A study of seven “moderately” experienced readers (40 to 150 previous cases) found that both CAD paradigms improved diagnostic performance significantly, especially concurrent [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…For example, Mang and colleagues asked two “expert” and two “nonexpert” observers to interpret CTC from 52 patients using second-read CAD, finding that CAD was beneficial only for the less experienced readers [ 6 ]. The same group also compared concurrent and second-read CAD paradigms, but in “moderately” experienced radiologists alone [ 12 ]. A subsequent study by Petrick and co-workers also investigated the second-read paradigm using four readers, two of whom were experienced [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

et al. 2015

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ObjectivesTo quantify the incremental benefit of computer-assisted-detection (CAD) for polyps, for inexperienced readers versus experienced readers of CT colonography.Methods10 inexperienced and 16 experienced radiologists interpreted 102 colonography studies unassisted and with CAD utilised in a concurrent paradigm. They indicated any polyps detected on a study sheet. Readers’ interpretations were compared against a ground-truth reference standard: 46 studies were normal and 56 had at least one polyp (132 polyps in total). The primary study outcome was the difference in CAD net benefit (a combination of change in sensitivity and change in specificity with CAD, weighted towards sensitivity) for detection of patients with polyps.ResultsInexperienced readers’ per-patient sensitivity rose from 39.1% to 53.2% with CAD and specificity fell from 94.1% to 88.0%, both statistically significant. Experienced readers’ sensitivity rose from 57.5% to 62.1% and specificity fell from 91.0% to 88.3%, both non-significant. Net benefit with CAD assistance was significant for inexperienced readers but not for experienced readers: 11.2% (95%CI 3.1% to 18.9%) versus 3.2% (95%CI -1.9% to 8.3%) respectively.ConclusionsConcurrent CAD resulted in a significant net benefit when used by inexperienced readers to identify patients with polyps by CT colonography. The net benefit was nearly four times the magnitude of that observed for experienced readers. Experienced readers did not benefit significantly from concurrent CAD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

et al. 2015

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“…The utility of computer-aided detection (CAD) has already been demonstrated in computed tomography (CT) colonography, with a reported sensitivity for polyps < 10 mm in size close to 80 % [13]. CAD has also been shown to increase the performance of radiologists, especially those who are non-expert or moderately experienced [14]. Although the performance of WM-DOVA in its current version is slightly lower than these results, it is impacted by the presence of other elements in the endoscopic luminal scene (vessels, specularities) that are not present in the radiological image and, once mitigated, we could expect an improvement in the results.…”

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confidence: 99%

Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps

et al. 2016

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“…The PAICS is proven to be valuable in assisting sonologists in recognizing abnormal fetal intracranial patterns, achieving improvements of 8% and 9% in average ACC when using concurrent and second modes, respectively. Concurrent reading and second reading are usually utilized in CAD-assisted reading in other fields, such as breast cancer diagnosis on automated breast US 11 , 13 , polyp detection in CT colonography 14 , 15 , and lung nodule identification on multidetector CT 12 . This is the first implementation in obstetric AI assistant scenarios.…”

Section: Discussionmentioning

confidence: 99%

Deep learning system improved detection efficacy of fetal intracranial malformations in a randomized controlled trial

Lin,

Zhou,

Lei

et al. 2023

npj Digit. Med.

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Congenital malformations of the central nervous system are among the most common major congenital malformations. Deep learning systems have come to the fore in prenatal diagnosis of congenital malformation, but the impact of deep learning-assisted detection of congenital intracranial malformations from fetal neurosonographic images has not been evaluated. Here we report a three-way crossover, randomized control trial (Trial Registration: ChiCTR2100048233) that assesses the efficacy of a deep learning system, the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS), in assisting fetal intracranial malformation detection. A total of 709 fetal neurosonographic images/videos are read interactively by 36 sonologists of different expertise levels in three reading modes: unassisted mode (without PAICS assistance), concurrent mode (using PAICS at the beginning of the assessment) and second mode (using PAICS after a fully unaided interpretation). Aided by PAICS, the average accuracy of the unassisted mode (73%) is increased by the concurrent mode (80%; P < 0.001) and the second mode (82%; P < 0.001). Correspondingly, the AUC is increased from 0.85 to 0.89 and to 0.90, respectively (P < 0.001 for all). The median read time per data is slightly increased in concurrent mode but substantially prolonged in the second mode, from 6 s to 7 s and to 11 s (P < 0.001 for all). In conclusion, PAICS in both concurrent and second modes has the potential to improve sonologists’ performance in detecting fetal intracranial malformations from neurosonographic data. PAICS is more efficient when used concurrently for all readers.

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CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography

Cited by 10 publications

References 36 publications

Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

Assessment of the Incremental Benefit of Computer-Aided Detection (CAD) for Interpretation of CT Colonography by Experienced and Inexperienced Readers

Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps

Deep learning system improved detection efficacy of fetal intracranial malformations in a randomized controlled trial

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