Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

Yongping, Liang; Zhang, Juan; Zhou, Ping; Zhao, Yongfeng; Liu, Wengang; Shi, Yifan

doi:10.2196/18251

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…S‐Detect is a CAD software that can realize automatic segmentation and analysis of US morphological descriptions and generate the decisions automatically. In previous studies on the diagnostic efficiency of S‐Detect software for breast lesions, most of them take BI‐RADS 4a (benign, 3 or lower) as the cutoff value, 16–20 and two studies take BI‐RADS 4b (benign, 4a or lower) as the cutoff value 22,31 . BI‐RADS 4a lesions mainly include some atypical benign and malignant lesions, with a malignant rate of 2–10% 8 .…”

Section: Discussionmentioning

confidence: 99%

“…At present, some studies have confirmed the feasibility of S‐Detect in differentiating breast masses 15–17 . Some studies have compared the diagnostic performance between S‐Detect and radiologists with different experiences 16–22 . A few studies have explored the value of S‐Detect in reducing unnecessary biopsy of experienced 23 and less experienced radiologists 21 .…”

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confidence: 99%

“…[15][16][17] Some studies have compared the diagnostic performance between S-Detect and radiologists with different experiences. [16][17][18][19][20][21][22] A few studies have explored the value of S-Detect in reducing unnecessary biopsy of experienced 23 and less experienced radiologists. 21 No study has explored whether the additional value of S-Detect for radiologists in differentiating benign from malignant breast masses and reducing unnecessary biopsy is influenced by the experience of radiologists.…”

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confidence: 99%

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The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

Wei

Zeng

Wang

et al. 2021

J of Ultrasound Medicine

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Objectives-To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists.Methods-From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed.Results-After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P = .006; 36.8% vs 28.2%, P = .033), but not for experienced radiologists (P > .05).Conclusions-Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

Wei

Zeng

Wang

et al. 2021

J of Ultrasound Medicine

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“…This study demonstrated the feasibility of using a mobile app to monitor the recovery status of patients with drains and to assist patients and caregivers in detecting abnormalities in a timely manner. Remote apps could support self-care and allow close follow-up [32][33][34]. We also identified an unexpected reduction in the rates of hospital return and readmission in the SurgCare group (6.4%) compared with the conventional group (25.9%) (P=.02).…”

Section: Discussionmentioning

confidence: 81%

An Image-Based Mobile Health App for Postdrainage Monitoring: Usability Study (Preprint)

Liao¹,

Wu²,

Cheng³

et al. 2020

Preprint

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BACKGROUND The application of mobile health (mHealth) platforms to monitor recovery in the postdischarge period has increased in recent years. Despite widespread enthusiasm for mHealth, few studies have evaluated the usability and user experience of mHealth in patients with surgical drainage. OBJECTIVE Our objectives were to (1) develop an image-based smartphone app, SurgCare, for postdrainage monitoring and (2) determine the feasibility and clinical value of the use of SurgCare by patients with drainage. METHODS We enrolled 80 patients with biliary or peritoneal drainage in this study. A total of 50 patients were assigned to the SurgCare group, who recorded drainage monitoring data with the smartphone app; and 30 patients who manually recorded the data were assigned to the conventional group. The patients continued to record data until drain removal. The primary aim was to validate feasibility for the user, which was defined as the proportion of patients using each element of the system. Moreover, the secondary aim was to evaluate the association of compliance with SurgCare and the occurrence of unexpected events. RESULTS The average submission duration was 14.98 days, and the overall daily submission rate was 84.2%. The average system usability scale was 83.7 (SD 3.5). This system met the definition of “definitely feasible” in 34 patients, “possibly feasible” in 10 patients, and “not feasible” in 3 patients. We found that the occurrence rates of complications in the SurgCare group and the conventional group were 6% and 26%, respectively, with statistically significant differences <i>P</i>=.03. The rate of unexpected hospital return was lower in the SurgCare group (6%) than in the conventional groups (26%) (<i>P</i>=.03). CONCLUSIONS Patients can learn to use a smartphone app for postdischarge drainage monitoring with high levels of user satisfaction. We also identified a high degree of compliance with app-based drainage-recording design features, which is an aspect of mHealth that can improve surgical care.

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“…Deep learning (DL) is a subset of machine learning that uses an advanced form of artificial neural networks; the use of DL has impacted health care [1,2]. Numerous applications of DL in medicine, such as computer-aided diagnosis, have been studied [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

Cheng¹,

Chen²,

Cheng³

et al. 2020

JMIR Med Inform

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Background Hip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. Objective The objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. Methods The HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. Results With the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; P<.001), specificity (physician alone, median 90%; HAI, median 95%; P<.001), accuracy (physician alone, median 90%; HAI, median 96%; P<.001), and human-algorithm agreement [physician alone κ, median 0.69 (IQR 0.63-0.74); HAI κ, median 0.80 (IQR 0.76-0.82); P<.001. With the help of the HAI system, the primary physicians showed significant improvement in their diagnostic performance to levels comparable to those of consulting physicians, and both the experienced and less-experienced physicians benefited from the HAI system. After the HAI system had been applied in 3 departments for 5 months, 587 images were examined. The sensitivity, specificity, and accuracy of the HAI system for detecting hip fractures were 97%, 95.7%, and 96.08%, respectively. Conclusions HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.

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Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

Cited by 7 publications

References 27 publications

The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

The Added Value of a Computer‐Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience

An Image-Based Mobile Health App for Postdrainage Monitoring: Usability Study (Preprint)

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

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