Dataset Information

Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study.

ABSTRACT:

Background

Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA).

Methods

Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: computer depth learning group (n=43) and experimental group (n=42). For the training set (13,144 sheets) and the test set (313 sheets), the auxiliary diagnosis method was obtained and applied to the experimental group.

Results

Among the participants, a good sensitivity of 90.9% and an average false positive of 2.0 were obtained by using the deep learning detection method proposed in this paper, and the detection rate was positively correlated with arterial grade.

Conclusions

The computer-aided diagnostic method proposed in this paper can effectively improve the detection rate of PE, especially for the detection of intra-arterial embolism above grade 3. However, because of the high misdetection rate, more in-depth learning datasets are needed for the detection of embolism below grade 3.

SUBMITTER: Li X

PROVIDER: S-EPMC8184458 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study.

Li Xiang X Wang Xiang X Yang Xin X Lin Yi Y Huang Zengfa Z

Annals of translational medicine 20210501 10

<h4>Background</h4>Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA).<h4>Methods</h4>Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: compu ...[more]

PMID: 34164472

Similar Datasets

Project description:To evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice. This was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality. Overall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]). Instead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists. • Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.

Project description:ObjectiveTo test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for full-field digital mammography (FFDM) when applied to synthetic mammography (SM).Materials and methodsWe analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics. The results of SM and FFDM were compared.ResultsAI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs. 91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts.ConclusionAI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM. Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Dataset Information

Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study.

Background

Methods

Results

Conclusions

Publications

Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets