Deep Learning Pearls
- “In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.”
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Xueyan Mei et al.
Nat Med (2020). https://doi.org/10.1038/s41591-020-0931-3 - "We believe implementation of the joint algorithm discussed above could aid in both issues. First, the AI algorithm could evaluate the CT immediately after completion. Second, the algorithm outperformed radiologists in identifying patients positive for COVID-19, demonstrating normal CT results in the early stage. Third, the algorithm performed equally well in sensitivity (P = 0.05) in the diagnosis of COVID-19 as compared to a senior thoracic radiologist. Specifically, the joint algorithm achieved a statistically significant 6% (P = 0.00146) and 12% (P < 1 × 10−4) improvement in AUC as compared to the CNN model using only CT images and the MLP model using only clinical information respectively.”
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Xueyan Mei et al.
Nat Med (2020). https://doi.org/10.1038/s41591-020-0931-3 - "In conclusion, these results illustrate the potential role for a highly accurate AI algorithm for the rapid identification of COVID-19 patients, which could be helpful in combating the current disease outbreak. We believe the AI model proposed, which combines CT imaging and clinical information and shows equivalent accuracy to a senior chest radiologist, could be a useful screening tool to quickly diagnose infectious diseases such as COVID-19 that does not require radiologist input or physical tests.”
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Xueyan Mei et al.
Nat Med (2020). https://doi.org/10.1038/s41591-020-0931-3
- Summary: AI assistance improved radiologists’ performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT.
Key Results:
• An AI model had higher test accuracy (96% vs 85%, p <0.0010, sensitivity (95% vs 79%, p <0.0010 and specificity (96% vs 88%, p=0.002) than radiologists.
• In an independent test set, our AI model achieved an accuracy of 87%, sensitivity of 89% and specificity of 86%.
• With AI assistance, the radiologists achieved a higher average accuracy (90% vs 85, p=0.001)
AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT Harrison X.
Bai HX et al.
Radiology 2020 (in press) - “AI assistance improved radiologists’ performance in distinguishing COVID-19 from non-COVID pneumonia on chest CT.”
AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT Harrison X.
Bai HX et al.
Radiology 2020 (in press) - "The COVID-19 pandemic poses a number of challenges to the Artificial Intelligence (AI) Community. Among these challenges are “Can AI help track and predict the spread of the infection?”, “Can AI help in making diagnoses and prognoses?”, “Can it be used in the search for treatments and a vaccine?” and “Can it be used for social control?” This paper is an attempt to provide an early review of how AI have so far been contributing in this regard, and to note limitations, constraints, and pitfalls. These include a lack of data, too much (noisy and outlier) data, and growing tension between data privacy concerns and public health imperatives.”
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naude W
AI Soc. 2020 Apr 28 : 1–5. - • Tracking and prediction
• Diagnosis and Prognosis
• Treatment and vaccines
• Social control
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naude W
AI Soc. 2020 Apr 28 : 1–5. - “This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages.”
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naude W
AI Soc. 2020 Apr 28 : 1–5.