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Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound.

ABSTRACT: The value of computer-aided diagnosis (CAD) and computer-assisted techniques equipped with different TIRADS remains ambiguous. Parallel diagnosis performances of computer-assisted subjective assessments and CAD were compared based on AACE, ATA, EU, and KSThR TIRADS. CAD software computed the diagnosis of 162 thyroid nodule sonograms. Two raters (R₁ and R₂) independently rated the sonographic features of the nodules using an online risk calculator while blinded to pathology results. Diagnostic efficiency measures were calculated based on the final pathology results. R₁ had higher diagnostic performance outcomes than CAD with similarities between KSThR (SEN: 90.3% vs. 83.9%, p = 0.57; SPEC: 46% vs. 51%, p = 0.21; AUROC: 0.76 vs. 0.67, p = 0.02), and EU (SEN: 85.5% vs. 79%, p = 0.82; SPEC: 62% vs. 55%, p = 0.27; AUROC: 0.74 vs. 0.67, p = 0.06). Similarly, R₂ had higher AUROC and specificity but lower sensitivity than CAD (KSThR-AUROC: 0.74 vs. 0.67, p = 0.13; SPEC: 61% vs. 46%, p = 0.02 and SEN: 75.8% vs. 83.9%, p = 0.31, and EU-AUROC: 0.69 vs. 0.67, p = 0.57, SPEC: 64% vs. 55%, p = 0.19, and SEN: 71% vs. 79%, p = 0.51, respectively). CAD had higher sensitivity but lower specificity than both R₁ and R₂ with AACE for 114 specified nodules (SEN: 92.5% vs. 88.7%, p = 0.50; 92.5% vs. 79.3%, p = 0.02, and SPEC: 26.2% vs. 54.1%, p = 0.001; 26.2% vs. 62.3%, p < 0.001, respectively). All diagnostic performance outcomes were comparable for ATA with 96 specified nodules. Computer-assisted subjective interpretation using KSThR is more ideal for ruling out papillary thyroid carcinomas than CAD. Future larger multi-center and multi-rater prospective studies with a diverse representation of thyroid cancers are necessary to validate these findings.

SUBMITTER: Chambara N

PROVIDER: S-EPMC8621517 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Importance:The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. Objective:To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. Data Sources:The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. Study Selection:Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. Data Extraction and Synthesis:Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. Main Outcomes and Measures:Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. Results:The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P?<?.001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P?=?.67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. Conclusions and Relevance:Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.

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Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound.

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