An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest CT

Purpose

In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.

Methods

The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).

Results

The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items).

Conclusion

This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfectedhttps://bit.ly/3MJrVRi

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Value of Teledermoscopy in Primary Healthcare Centers: Preliminary Results of the TELESPOT Project in Belgium

INTRODUCTION

Teledermoscopy using smartphone-based applications is becoming more and more important in a setting of increasing frequency of skin cancer and difficult access to specialized care. The TELESPOT project aimed to provide rapid diagnosis and speed up patient flow between primary healthcare centers and a tertiary care center in Belgium. The aim of the present study is to describe the development of an in-house smartphone-based dermoscopy application, evaluate its real-life value in a series of primary healthcare centers, and present preliminary diagnostic data.

METHODS

Modified Likert scales were used to assess patient and general practitioner (GP) satisfaction rates for the system. Furthermore, a total of 105 photographic and dermoscopic images were acquired in a series of 80 patients at participating centers.

RESULTS

Overall, patient and GP satisfaction levels were 89% and 94%, respectively. High-priority management was recommended in 7.6% of cases (8/105: 3 basal cell carcinoma, 1 primary cutaneous B-cell lymphoma, 1 Spitz melanocytic nevus, 1 congenital nevus, 1 in situ melanoma, and 1 invasive melanoma, proven by histology).

CONCLUSIONS

The primary healthcare centers were highly satisfied with the TELESPOT project in terms of user-friendliness, efficacy, and reliability as well as in providing a reinforced image of first-line medicine efforts in combating skin cancer.

[TeleSPOT Project : early detection of melanoma by teledermoscopy in general practice]

Since decades the incidence of skin cancer is clearly rising. This alarming trend also applies to melanoma. It represents the 4th most common cancer in women and 6th in men in 2015. Early recognition and treatment reduce both morbidity and mortality. Screening is the cornerstone of secondary prevention. However, access to reliable and rapid diagnosis is hampered by several factors, including accessibility to specialized medicine. One of the solutions to this problem is to collaborate with the first-line medical care through a teledermatology system. The TeleSPOT project, Teledermoscopy Smartphone-based Pigmented lesion diagnosis Online Taskforce, aims to provide a remote diagnostic aid by dermatologists to distinguish suspect pigmented skin lesions and accelerate their management.