Factors Correlated with the Accuracy of Colposcopy-Directed Biopsy: A Systematic Review and Meta-Analysis.

PURPOSE/AIM OF THE STUDY: Colposcopy-directed cervical biopsy has played a major role in diagnosing cervical lesions. The precision of colposcopy-guided biopsy has been questioned. We analyzed several factors that may be correlated with the accuracy of biopsy. METHODS: PubMed, EMBASE were searched from January 1, 1998 to March 1, 2020. Odds ratio with 95% confidence intervals (CIs) were calculated. SELECTION CRITERIA: Included studies evaluated factors correlated with the accuracy of biopsy and patients' final diagnosis was established by histological examination of the specimen obtained by conization, loop electrosurgical excision procedure (LEEP), or colpohysterectomy. RESULTS: A total of 10 studies were selected for the systematic review and meta-analysis. The pooled analysis indicated that the diagnostic inaccuracies of colposcopy-directed cervical biopsy were magnified in women who were 50 years of age or older. Postmenopausal status and transformation zone 3 type were also associated with the diagnostic inaccuracies of colposcopy-directed biopsy. High-grade squamous intraepithelial lesions had better concordance rates than low-grade squamous intraepithelial lesions. The number of vaginal deliveries, number of biopsies, and HPV type were associated with biopsy underdiagnosis and biopsy overestimation. CONCLUSIONS: This meta-analysis found that the correlation between the histological findings at biopsy and after surgical treatment was influenced by women's age, menopausal status, and the transformation zone type. The diagnostic efficacy was also better for high-grade squamous intraepithelial lesions than for low-grade squamous intraepithelial lesions. Further large-scale randomized clinical trials are required to analyze the factors correlated with biopsy underdiagnosis and biopsy overestimation.

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation.

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic. METHODS: We proposed an interpretable one-dimensional convolutional neural network (1DCNN) to detect four most manifestation types of PVA (double triggering, ineffective efforts during expiration, premature cycling and delayed cycling) under pressure control ventilation mode and pressure support ventilation mode. A global average pooling (GAP) layer was incorporated with the 1DCNN model to highlight the sections of the respiratory waveform the model focused on when making a classification. Dilation convolution and batch normalization were introduced to the 1DCNN model for compensating the reduction of performance caused by the GAP layer. RESULTS: The proposed interpretable 1DCNN exhibited comparable performance with the state-of-the-art deep learning model in PVA detection. The F1 scores for the detection of four types of PVA under pressure control ventilation and pressure support ventilation modes were greater than 0.96. The critical sections of the waveform used to detect PVA were highlighted, and found to be well consistent with the understanding of the respective type of PVA by experts. CONCLUSIONS: The findings suggest that the proposed 1DCNN can help detect PVA, and enhance the interpretability of the classification process to help clinicians better understand the results obtained from deep learning technology.