Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography

Kozuka, Takenori; Matsukubo, Yuko; Tomoya, Kadoba; Oda, Tatsuya; Suzuki, Ayako; Hyodo, Toru; Im, Sung-Woon; Kaida, Hayato; Yagyu, Yukinobu; Tsurusaki, Masakatsu; Matsuki, Mitsuru; Ishii, Kazunari

doi:10.1007/s11604-020-01009-0

Cited by 31 publications

(18 citation statements)

References 32 publications

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“…Martini et al [ 27 ] reported improved interrater agreement (17–34%) in addition to improved mean reading time ( Table 2 c), when assisted by AI. Results for the effects of AI assistance on radiologists by Kozuka et al [ 22 ], Liu et al [ 26 ], and Singh et al [ 28 ] are also shown in Table 2 a,b, but only Kozuka et al [ 22 ] reported significant improvement (sensitivity from 68% to 85.1%, p < 0.01). In addition to change in accuracy, Liu et al [ 26 ] reported a reduction of reading time per patient from 15 min to 5–10 min without mentioning statistical significance.…”

Section: Resultsmentioning

confidence: 85%

“…We included a total of 38 studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] in our systematic review. The QUADAS-2 tool is presented in Figure 1 , and a PRISMA flowchart of the literature search is presented in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…We divided the studies into two groups: The first group, consisting of 19 studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], used an AI-based device as a concurrent reader in an observer test, where the observers were tasked with diagnosing images with assistance from an AI-based device, while not being allowed (blinded) to see their initial diagnosis made without assistance from AI ( Table 1 a). The second group, consisting of 20 studies [ 19 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] used the AI-based device as a second reader in an un-blinded sequential observer test, thus allowing observers to see and change their original un-assisted diagnosis ( Table 1 b).…”

Section: Resultsmentioning

confidence: 99%

“…Beyer et al [ 19 ], de Hoop et al [ 20 ], Koo et al [ 21 ], Kozuka et al [ 22 ], Lee et al [ 23 ], Li et al [ 24 ], Li et al [ 25 ], Liu et al [ 26 ], Martini et al [ 27 ], and Singh et al [ 28 ] used AI-based devices to assist with detection of pulmonary nodules. Even though de Hoop et al [ 20 ] found a slight increase in sensitivity in residents (49% to 51%) and change in radiologists (63% to 61%) for nodule detection, both changes were not statistically significant ( Table 2 a).…”

Section: Resultsmentioning

confidence: 99%

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The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Pehrson

Lauridsen

et al. 2021

Diagnostics

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Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Pehrson

Lauridsen

et al. 2021

Diagnostics

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“…Change in reading time when using ML or DL has been evaluated in other studies, with different imaging modalities and tumor types. A decrease in reading time of 11% has been reported in the detection of lung nodules, using DL 18 . The use of DL support in breast tomosynthesis resulted in a mean decrease in reading time from 41 to 36 seconds, a mean decrease of 11%, with a range of reported decreases of between 14% and 52.7% 19–21 .…”

Section: Discussionmentioning

confidence: 92%

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer

Rockall

Johnson

et al. 2023

Invest Radiol

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ObjectivesWhole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times.Materials and MethodsA retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013–September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated.ResultsFour hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (−1.5% difference; 95% confidence interval [CI], −6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (−4.0% difference; 95% CI, −13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, −15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, −7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, −2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [−1.7% difference; 95% CI, −5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, −22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker (P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and ...

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Radiologists with and without deep learning–based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses

et al. 2022

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Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography

Cited by 31 publications

References 32 publications

The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer

Radiologists with and without deep learning–based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses

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