Methodologic Guide for Evaluating Clinical Performance and Effect of                    Artificial Intelligence Technology for Medical Diagnosis and                    Prediction

Park, Seong Ho; Han, Kyunghwa

doi:10.1148/radiol.2017171920

Cited by 592 publications

(461 citation statements)

References 48 publications

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“…According to the literature, disease prevalence affects the probability predictions of DCNNs . However, the prevalence of benign against atypical/anaplastic lesions in our database closely resembles the prevalence in the general population .…”

Section: Discussionmentioning

confidence: 51%

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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Background Grading of meningiomas is important in the choice of the most effective treatment for each patient. Purpose To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. Study Type Retrospective. Population In all, 117 meningioma‐affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. Field Strength/Sequence 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2). Assessment WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception‐V3 and AlexNet DCNNs was tested on ADC maps and PCT1W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. Statistical Test Leave‐one‐out cross‐validation. Results The application of the Inception‐V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88–0.98). Remarkably, only 1/38 WHO Grade II–III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59–0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II–III cases. The discriminating accuracy of both DCNNs on postcontrast T1W images was low, with Inception‐V3 displaying an AUC of 0.68 (95% CI, 0.59–0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45–0.64). Data Conclusion DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1W images. Level of evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152–1159.

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Section: Discussionmentioning

confidence: 51%

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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“…However, this study population underwent surgery before the new reporting system was established, thus information regarding the proportion of each tumor component was not available from the patients’ electronic medical records. A second reason may be the spectrum bias among different studies, which is inevitable in small retrospective studies …”

Section: Discussionmentioning

confidence: 99%

Growth of thymic epithelial tumors and thymic cysts: Differential radiological points

et al. 2019

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Background The growth rate of thymic epithelial tumors (TETs) and thymic cysts was investigated to determine whether they can be differentiated and clinico‐radiological predictors of interval growth was identified. Methods This retrospective study included 122 patients with pathologically proven thymic cysts ( n = 56) or TETs ( n = 66) who underwent two serial chest computed tomography scans at least eight weeks apart. Average diameters and attenuation were measured, volume‐doubling times (VDTs) were calculated, and clinical characteristics were recorded. VDTs were compared using the log‐rank test. Predictors of growth were analyzed using the log‐rank test and Cox regression analysis. Results The frequency of growth did not differ significantly between TETs and thymic cysts ( P = 0.279). The VDT of thymic cysts (median 324 days) was not significantly different from that of the TETs (median 475 days; P = 0.808). Water attenuation (≤ 20 Hounsfield units) predicted growth in thymic cysts ( P = 0.016; hazard ratio 13.2, 95% confidence interval 1.6–107.3), while lesion size (> 17.2 mm) predicted growth in TETs ( P = 0.008 for size, P = 0.029 for size*time). For the growing lesions, the positive and negative predictive values of water attenuation for thymic cysts were 93% and 80%, respectively. Conclusion The frequencies of interval growth and VDTs were indistinguishable between TETs and thymic cysts. Water attenuation and lesion size predicted growth in thymic cysts and TETs, respectively. Among the growing lesions, water attenuation was a differential feature of thymic cysts.

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“…Fourth, conduct the study in an international, multicenter setting to ensure reproducibility of the results. This point is especially important when researching CAD because the evaluation should be conducted outside the institution where the “machine” images are obtained . Apart from the general study design, the efficacy of CAD should be evaluated for all types of colorectal lesions.…”

Section: Future Directionsmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

111

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Background and Aim Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.

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Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

Cited by 592 publications

References 48 publications

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Growth of thymic epithelial tumors and thymic cysts: Differential radiological points

Artificial intelligence and colonoscopy: Current status and future perspectives

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