Researchers at King’s College London, Massachusetts General Hospital and health science company ZOE have developed an artificial intelligence diagnostic that can predict whether someone is likely to have COVID-19 based on their symptoms.
Their findings are published today in Nature Medicine.
The AI model uses data from the COVID Symptom Study app to predict COVID-19 infection, by comparing people’s symptoms and the results of traditional COVID tests.
Researchers say this may provide help for populations where access to testing is limited. Two clinical trials in the UK and the US are due to start shortly.
More than 3.3 million people globally have downloaded the app and are using it to report daily on their health status, whether they feel well or have any new symptoms such as persistent cough, fever, fatigue and loss of taste or smell (anosmia).
In this study, the researchers analysed data gathered from just under 2.5 million people in the UK and US who had been regularly logging their health status in the app, around a third of whom had logged symptoms associated with COVID-19.
Of these, 18,374 reported having had a test for coronavirus, with 7,178 people testing positive.
The research team investigated which symptoms known to be associated with COVID-19 were most likely to be associated with a positive test.
They found a wide range of symptoms compared to cold and flu, and warn against focusing only on fever and cough. Indeed, they found loss of taste and smell (anosmia) was particularly striking, with two thirds of users testing positive for coronavirus infection reporting this symptom compared with just over a fifth of the participants who tested negative.
The findings suggest that anosmia is a stronger predictor of COVID-19 than fever, supporting anecdotal reports of loss of smell and taste as a common symptom of the disease.
The researchers then created a mathematical model that predicted with nearly 80% accuracy whether an individual is likely to have COVID-19 based on their age, sex and a combination of four key symptoms: loss of smell or taste, severe or persistent cough, fatigue and skipping meals.
Applying this model to the entire group of over 800,000 app users experiencing symptoms predicted that just under a fifth of those who were unwell (17.42%) were likely to have COVID-19 at that time.
Researchers suggest that combining this AI prediction with widespread adoption of the app could help to identify those who are likely to be infectious as soon as the earliest symptoms start to appear, focusing tracking and testing efforts where they are most needed.
Professor Tim Spector from King’s College London said: “Our results suggest that loss of taste or smell is a key early warning sign of COVID-19 infection and should be included in routine screening for the disease.
We strongly urge governments and health authorities everywhere to make this information more widely known, and advise anyone experiencing sudden loss of smell or taste to assume that they are infected and follow local self-isolation guidelines.”
COVID-19 presentation, which began with the reporting of unknown causes of pneumonia in Wuhan, Hubei province of China on December 31, 2019, has rapidly become a pandemic [[1], [2], [3]].
The disease is named COVID-19 and the virus is termed SARS-CoV-2. This new virus spread from Wuhan to much of China in 30 days [4]. The United States of America [5], where the first seven cases were reported on January 20, 2020, reached over 300,000 by the 5th of April 2020.
Most coronaviruses affect animals, but they can also be transmitted to humans because of their zoonotic nature. Severe acute respiratory syndrome Coronavirus (SARS-CoV) and the Middle East respiratory syndrome Coronavirus (MERS-CoV) have caused severe respiratory disease and death in humans [6].
The typical clinical features of COVID-19 include fever, cough, sore throat, headache, fatigue, muscle pain, and shortness of breath [7].
The most common test technique currently used for COVID-19 diagnosis is a real-time reverse transcription-polymerase chain reaction (RT-PCR). Chest radiological imaging such as computed tomography (CT) and X-ray have vital roles in early diagnosis and treatment of this disease [8].
Due to the low RT-PCR sensitivity of 60%–70%, even if negative results are obtained, symptoms can be detected by examining radiological images of patients [9,10]. It is stated that CT is a sensitive method to detect COVID-19 pneumonia, and can be considered as a screening tool with RT-PRC [11].
CT findings are observed over a long interval after the onset of symptoms, and patients usually have a normal CT in the first 0–2 days [12]. In a study on lung CT of patients who survived COVID-19 pneumonia, the most significant lung disease is observed ten days after the onset of symptoms [13].
At the beginning of the pandemic, Chinese clinical centers had insufficient test kits, which are also producing a high rate of false-negative results, so doctors are encouraged to make a diagnosis only based on clinical and chest CT results [12,14].
CT is widely used for COVID-19 detection in countries such as Turkey, where a low number of test kits at onset of the pandemic were available. Researchers state that combining clinical image features with laboratory results may help in early detection of COVID-19 [6,11,[15], [16], [17]].
Radiologic images obtained from COVID-19 cases contain useful information for diagnostics. Some studies have encountered changes in chest X-ray and CT images before the beginning of COVID-19 symptoms [18].
Significant discoveries have been realized by investigators in imaging studies of COVID-19. Kong et al. [6] observed right infrahilar airspace opacities in a COVID-19 patient. Yoon et al. [19] reported that one in three patients studied had a single nodular opacity in the left lower lung region.
In contrast, the other two had four and five irregular opacities in both lungs. Zhao et al. [16] not only found ground-glass opacities (GGO) or mixed GGO in most of the patients, but they also observed a consolidation, and vascular dilation in the lesion. Li and Xia [17] reported GGO and consolidation, interlobular septal thickening and air bronchogram sign, with or without vascular expansion, as common CT features of COVID-19 patients.
Peripheral focal or multifocal GGO affecting both lungs in 50%–75% of patients is another observation [9]. Similarly, Zu et al. [8] and Chung et al. [6] discovered that 33% of chest CTs can have rounded lung opacities. In Fig. 1 , chest X-ray images taken at days 1, 4, 5 and 7 for a 50-year-old COVID-19 patient with pneumonia are given, and explanations of these images are also provided [20].
Application of machine learning methods for automatic diagnosis in the medical field have recently gained popularity by becoming an adjunct tool for clinicians [[21], [22], [23], [24], [25]].
Deep learning, which is a popular research area of artificial intelligence (AI), enables the creation of end-to-end models to achieve promised results using input data, without the need for manual feature extraction [26,27].
Deep learning techniques have been successfully applied in many problems such as arrhythmia detection [[28], [29], [30]], skin cancer classification [31,32], breast cancer detection [33,34], brain disease classification [35], pneumonia detection from chest X-ray images [36], fundus image segmentation [37], and lung segmentation [38,39].
The COVID-19 epidemic’s rapid rise has necessitated the need for expertise in this field. This has increased interest in developing the automated detection systems based on AI techniques.
It is a challenging task to provide expert clinicians to every hospital due to the limited number of radiologists. Therefore, simple, accurate, and fast AI models may be helpful to overcome this problem and provide timely assistance to patients.
Although radiologists play a key role due to their vast experience in this field, the AI technologies in radiology can be assistive to obtain accurate diagnosis [40]. Additionally, AI approaches can be useful in eliminating disadvantages such as insufficient number of available RT-PCR test kits, test costs, and waiting time of test results.
Recently, many radiology images have been widely used for COVD-19 detection. Hemdan et al. [41] used deep learning models to diagnose COVID-19 in X-ray images and proposed a COVIDX-Net model comprising seven CNN models. Wang and Wong [42] proposed a deep model for COVID19 detection (COVID-Net), which obtained 92.4% accuracy in classifying normal, non-COVID pneumonia, and COVID-19 classes. Ioannis et al. [43] developed the deep learning model using 224 confirmed COVID-19 images.
Their model achieved 98.75% and 93.48% success rates for two and three classes, respectively. Narin et al. [44] achieved a 98% COVID-19 detection accuracy using chest X-ray images coupled with the ResNet50 model. Sethy and Behera [45] classified the features obtained from various convolutional neural network (CNN) models with support vector machine (SVM) classifier using X-ray images.
Their study states that the ResNet50 model with SVM classifier provided the best performance. Finally, there are also several recent studies on COVID-19 detection that employed various deep learning models with CT images [[46], [47], [48], [49], [50], [51]].
In this study, a deep learning model is proposed for the automatic diagnosis of COVID-19. The proposed model has an end-to-end architecture without using any feature extraction methods, and it requires raw chest X-ray images to return the diagnosis.
This model is trained with 125 chest X-ray images, which are not in a regular form and were obtained hastily. Diagnostic tests performed after 5–13 days are found to be positive in recovered patients [52]. This crucial finding shows us that recovered patients may continue to spread the virus.
Therefore, more accurate methods for the diagnosis is needed. One of the most important disadvantages of chest radiography analyses is an inability to detect the early stages of COVID-19, as they do not have sufficient sensitivity in GGO detection [8]. However, well-trained deep learning models can focus on points that are not noticeable to the human eye, and may serve to reverse this perception.
Evaluation of the model outputs by the radiologist
This section includes the interpretation of results of the DarkCovidNet model by an expert radiologist. The DarkCovidNet model is designed for the automatic detection of COVID-19 using X-ray images, without requiring any handcrafted feature extraction techniques.
The developed model helps to provide a second opinion to expert radiologists in health centers. It may significantly reduce the workload of clinicians and assist them to make an accurate diagnosis in their daily routine work.
The proposed model can save time (the diagnostic process is fast); hence specialists can focus on more critical cases. In this work, we have shared the outputs of the model with expert radiologists to confirm model robustness. We shared the top prediction errors of the model and the actual labels of the X-ray dataset with radiologists.
Additionally, we also used the Grad-CAM [61] heat map approach to visually depict decisions made by the deep model. The heatmap highlights important areas that the model emphasizes on the X-ray.
In this way, we have ensured that the outcome of the model is approved by a radiologist. In the clinical setting, an illustration of the model which can provide the second opinion to radiologists is shown in Fig. 9 .
The radiologist comments on the output of DarkCovidNet model are as follows:
- The model performed outstandingly in detecting COVID-19 cases for the binary class task.
- The DarkCovidNet model is successful in detecting COVID-19 findings.
- Clinically, pneumonia images are also later included in the study. Therefore, the model evaluated patients with COVID-19 as pneumonia. Since COVID-19 pneumonia is a subset of pneumonia diseases evaluated by the model, the diagnosis is correct, although the interpretation seems to be incorrect. Therefore, patients identified as COVID-19 are evaluated as pneumonia (see Fig. 10 (a)). For this reason, the success rate of the model in the multi-class classification problem is relatively low as compared to the binary class.
- The model is sensitive in detecting pneumonia disease. Although the model can predict pneumonia positively and marked as no findings in the dataset, this patient has a mass (see Fig. 10 (b)).
- The model made the incorrect predictions in poor quality X-ray imagery and in patients with acute respiratory distress syndrome (ARDS), in which the lung image is diffuse and much lung ventilation is lost (see Fig. 10 (c)).
- The model is useful to detect COVID-19 with presence of a heat map in normal subjects. Its effectiveness diminishes in pneumonia and ARDS cases. The heat map showed a greater concentration area in the X-rays of patients with COVID-19 than the area in which the disease is not seen (see Fig. 11).
- The model may be more useful evaluate the efficacy of treatment based on the heatmap. It can also assist experts in terms of diagnosis, follow-up, treatment, and isolation of patients.
Fig. 12 shows the difference between a few COVID and pneumonia case images. The following primary findings are frequently observed in the chest X-rays of COVID-19 patients [15].
- • Ground-glass opacities (GGO) (bilateral, multifocal, subpleural, peripheral, posterior, medial and basal).
- • A crazy paving appearance (GGOs and inter-/intra-lobular septal thickening).
- • Air space consolidation.
- • Bronchovascular thickening (in the lesion).
- • Traction bronchiectasis.
Similarly, chest X-ray findings of pneumonia patients are observed as follows [62].
- • Ground-glass opacities (GGO) central distribution, unilateral
- • Reticular opacity
- • Vascular thickening
- • Distribution more along the bronchovascular bundle
- • Bronchial wall thickening
In COVID-19, isolated lobar or segmental consolidation without GGO, multiple tiny pulmonary nodules, tree-in-bud, pneumothorax, cavitation, and hilar lymphadenopathy smoother interlobular septal thickening with pleural effusion are rare, while these findings can often be seen in pneumonia [9].
In the COVID-19 epidemic, radiological imaging plays an important role in addition to the diagnostic tests performed for the early diagnosis, treatment, and isolation stages of the disease. Chest radiography can detect a few characteristic findings in the lung associated with COVID-19.
Deep learning models are sensitive in detecting COVID-19 lung involvement and hence the diagnostic accuracy rate is high. During the evaluation of the model, X-ray radiographs of COVID-19 patients confirmed positive by the PCR Test are used. The model can easily detect GGO, consolidation areas, and nodular opacities, which are the pathognomic findings of patients for COVID-19 on X-ray radiography.
In COVID-19, bilateral, lower lobe, and peripheral involvement is observed, and the proposed model can detect localization of the lesion. These models are particularly important in identifying early stages of COVID-19 patients. Early diagnosis of the disease is important to provide immediate treatment and to prevent disease transmission. The models can also play an indispensable role in patients lacking early symptoms.
There is a margin of error in patients with diffuse late lung parenchyma and in patients with significantly reduced lung ventilation due to poor quality X-ray images. X-rays that are not of optimal quality are difficult to evaluate by radiologists.
The clinical and radiological images of later-stage patients are well established and it is easier to detect the findings by experts. The role of deep learning models is more prominent in screening and diagnosis when the infection is at its early stages.
The models can be readily used in healthcare centers. There is no need to wait long hours for the radiologists to screen the images. Thus, healthcare workers and patient relatives can focus on isolation of suspicious cases so that treatment can begin.
Hence, the spread of the disease can be significantly reduced. The patients can seek a second opinion if they are diagnosed as positive by our system. Hence, waiting time can be significantly reduced, and it will alleviate clinician workload.
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