Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.
Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed...
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2021
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oai:doaj.org-article:2b659181f8be4df491eea6c38b6c6b022021-12-02T20:10:58ZDevelopment and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.1932-620310.1371/journal.pone.0252882https://doaj.org/article/2b659181f8be4df491eea6c38b6c6b022021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252882https://doaj.org/toc/1932-6203Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.Thodsawit TiyarattanachaiTerapap ApiparakoonSanparith MarukatatSasima SukcharoenNopavut GeratikornsupukNopporn AnukulkarnkusolParit MekaroonkamolNatthaporn TanpowpongPamornmas SarakulRungsun RerknimitrRoongruedee ChaiteerakijPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252882 (2021) |
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Medicine R Science Q Thodsawit Tiyarattanachai Terapap Apiparakoon Sanparith Marukatat Sasima Sukcharoen Nopavut Geratikornsupuk Nopporn Anukulkarnkusol Parit Mekaroonkamol Natthaporn Tanpowpong Pamornmas Sarakul Rungsun Rerknimitr Roongruedee Chaiteerakij Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| description |
Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted. |
| format |
article |
| author |
Thodsawit Tiyarattanachai Terapap Apiparakoon Sanparith Marukatat Sasima Sukcharoen Nopavut Geratikornsupuk Nopporn Anukulkarnkusol Parit Mekaroonkamol Natthaporn Tanpowpong Pamornmas Sarakul Rungsun Rerknimitr Roongruedee Chaiteerakij |
| author_facet |
Thodsawit Tiyarattanachai Terapap Apiparakoon Sanparith Marukatat Sasima Sukcharoen Nopavut Geratikornsupuk Nopporn Anukulkarnkusol Parit Mekaroonkamol Natthaporn Tanpowpong Pamornmas Sarakul Rungsun Rerknimitr Roongruedee Chaiteerakij |
| author_sort |
Thodsawit Tiyarattanachai |
| title |
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| title_short |
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| title_full |
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| title_fullStr |
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| title_full_unstemmed |
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| title_sort |
development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/2b659181f8be4df491eea6c38b6c6b02 |
| work_keys_str_mv |
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