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Assessing Distinct Cognitive Workload Levels Associated with Unambiguous and Ambiguous Pronoun Resolutions in Human–Machine Interactions
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In: Brain Sci (2022)
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Does Basic Psychological Need Satisfaction Matter to College Students’ Sustained Volunteering? A Mixed-Methods Study
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In: International Journal of Environmental Research and Public Health; Volume 18; Issue 24; Pages: 13229 (2021)
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Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning ...
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Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study
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Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study
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In: BMJ Open (2020)
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Abstract:
OBJECTIVE: The aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm. METHODS: The study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents. PARTICIPANTS: A total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions. RESULTS: S-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%–48.67%, AUC: 0.62–0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%–76.11%, AUC: 0.71–0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643). CONCLUSIONS: With the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.
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Keyword:
Radiology and Imaging
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URL: http://www.ncbi.nlm.nih.gov/pubmed/32513885
https://doi.org/10.1136/bmjopen-2019-035757
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282415/
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| 13 |
Isolated cortical vein thrombosis after nitrous oxide use in a young woman: a case report
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In: BMC Neurol (2020)
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| 14 |
TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine
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In: Comput Math Methods Med (2020)
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| 15 |
Bimodal Benefits for Lexical Tone Recognition: An Investigation on Mandarin-speaking Preschoolers with a Cochlear Implant and a Contralateral Hearing Aid
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In: Brain Sci (2020)
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| 16 |
Context shapes early diversity in abstract thought.
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In: Proceedings of the National Academy of Sciences of the United States of America, vol 116, iss 28 (2019)
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