Jenifer Siegelman

Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestations of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

RATIONALE: Treatment of pulmonary nontuberculous mycobacteria, especially Mycobacterium abscessus, requires prolonged, multidrug regimens with high toxicity and suboptimal efficacy. Options for refractory disease are limited. OBJECTIVES: We reviewed the efficacy and toxicity of inhaled amikacin in patients with treatment-refractory nontuberculous mycobacterial lung disease. METHODS: Records were queried to identify patients who had inhaled amikacin added to failing regimens. Lower airway microbiology, symptoms, and computed tomography scan changes were assessed together with reported toxicity. MEASUREMENTS AND MAIN RESULTS: The majority (80%) of the 20 patients who met entry criteria were women; all had bronchiectasis, two had cystic fibrosis and one had primary ciliary dyskinesia. At initiation of inhaled amikacin, 15 were culture positive for M. abscessus and 5 for Mycobacterium avium complex and had received a median (range) of 60 (6, 190) months of mycobacterial treatment. Patients were followed for a median of 19 (1, 50) months. Eight (40%) patients had at least one negative culture and 5 (25%) had persistently negative cultures. A decrease in smear quantity was noted in 9 of 20 (45%) and in mycobacterial culture growth for 10 of 19 (53%). Symptom scores improved in nine (45%), were unchanged in seven (35%), and worsened in four (20%). Improvement on computed tomography scans was noted in 6 (30%), unchanged in 3 (15%), and worsened in 11 (55%). Seven (35%) stopped amikacin due to: ototoxicity in two (10%), hemoptysis in two (10%), and nephrotoxicity, persistent dysphonia, and vertigo in one each. CONCLUSIONS: In some patients with treatment-refractory pulmonary nontuberculous mycobacterial disease, the addition of inhaled amikacin was associated with microbiologic and/or symptomatic improvement; however, toxicity was common. Prospective evaluation of inhaled amikacin for mycobacterial disease is warranted.