Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: A systematic review.

Digital pathology had a recent growth, stimulated by the implementation of digital whole slide images (WSIs) in clinical practice, and the pathology field faces shortage of pathologists in the last few years. This scenario created fronts of research applying artificial intelligence (AI) to help pathologists. One of them is the automated diagnosis, helping in the clinical decision support, increasing efficiency and quality of diagnosis. However, the complexity nature of the WSIs requires special treatments to create a reliable AI model for diagnosis. Therefore, we systematically reviewed the literature to analyze and discuss all the methods and results in AI in digital pathology performed in WSIs on H&E stain, investigating the capacity of AI as a diagnostic support tool for the pathologist in the routine real-world scenario. This review analyzes 26 studies, reporting in detail all the best methods to apply AI as a diagnostic tool, as well as the main limitations, and suggests new ideas to improve the AI field in digital pathology as a whole. We hope that this study could lead to a better use of AI as a diagnostic tool in pathology, helping future researchers in the development of new studies and projects.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Federated Learning used for predicting outcomes in SARS-COV-2 patients.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

PURPOSE: To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. MATERIALS AND METHODS: A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. RESULTS: Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. CONCLUSIONS: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

68Ga-DOTATATE PET/CT, 99mTc-HYNIC-octreotide SPECT/CT, and whole-body MR imaging in detection of neuroendocrine tumors: a prospective trial.

UNLABELLED: There are different metabolic imaging methods, various tracers, and emerging anatomic modalities to stage neuroendocrine tumor (NET). We aimed to compare NET lesion detectability among (99m)Tc-hydrazinonicotinamide (HYNIC)-octreotide (somatostatin receptor scintigraphy [SSRS]) SPECT/CT, (68)Ga-DOTATATE PET/CT, and whole-body diffusion-weighted MR imaging (WB DWI). METHODS: Nineteen consecutive patients (34-77 y old; mean, 54.3 ± 10.4 y old; 10 men and 9 women) underwent SSRS SPECT/CT, (68)Ga-DOTATATE PET/CT, and WB DWI. Images were acquired with a maximum interval of 3 mo between them and were analyzed with masking by separate teams. Planar whole-body imaging and SPECT/CT were performed from thorax to pelvis using a double-head 16-slice SPECT/CT scanner 4 h after injection of 111-185 MBq of (99m)Tc-HYNIC-octreotide. (68)Ga-DOTATATE PET/CT was performed from head to feet using a 16-slice PET/CT scanner 45 min after injection of 185 MBq of tracer. WB DWI was performed in the coronal plane using a 1.5-T scanner and a body coil. The standard method of reference for evaluation of image performance was undertaken: consensus among investigators at the end of the study, clinical and imaging follow-up, and biopsy of suggestive lesions. RESULTS: McNemar testing was applied to evaluate the detectability of lesions using (68)Ga-DOTATATE PET/CT in comparison to SSRS SPECT/CT and WB DWI: a significant difference in detectability was noted for pancreas (P = 0.0455 and P = 0.0455, respectively), gastrointestinal tract (P = 0.0455 and P = 0.0455), and bones (P = 0.0082 and P = 0.0082). Two unknown primary lesions were identified solely by (68)Ga-DOTATATE PET/CT. (68)Ga-DOTATATE PET/CT, SSRS SPECT/CT, and WB DWI demonstrated, respectively, sensitivities of 0.96, 0.60, and 0.72; specificities of 0.97, 0.99, and 1.00; positive predictive values of 0.94, 0.96, and 1.00; negative predictive values of 0.98, 0.83, and 0.88; and accuracies of 0.97, 0.86, and 0.91. CONCLUSION: (68)Ga PET/CT seems to be more sensitive for detection of well-differentiated NET lesions, especially for bone and unknown primary lesions. NET can be staged with (68)Ga-DOTATATE PET/CT. WB DWI is an efficient new method with high accuracy and without ionizing radiation exposure. SSRS SPECT/CT should be used only when (68)Ga-DOTATATE PET/CT and WB DWI are not available.

Valoración eco-Doppler del arco palmar. ¿Es factible utilizar la arteria radial como injerto libre en la revascularización miocárdica? / Palmar eco-Doppler assessment. Is it feasible to use radial artery as free graft in myocardial revascularization?

El trabajo consiste en el estudio anatomofuncional de los arcos palmares a través del eco-Doppler. Para ello se realizaron pruebas funcionales como el ejercicio de la mano con clampeo radial, tomando imágenes pre y posclampeo para verificar el sentido del flujo, como así también semidieron las velocidades pre, intra y posclampeo. Durante las pruebas mencionadas se registró la oximetría de pulso en el dedo índice de la mano evaluada. Se halló que en el 90,5 por ciento de los pacientes estudiados se puede realizar la exéresis de la arteria radial sin que ocurran complicaciones isquémicas, confirmando así la validez del presente análisis

Valoración eco-Doppler del arco palmar. ¿Es factible utilizar la arteria radial como injerto libre en la revascularización miocárdica? / Palmar eco-Doppler assessment. Is it feasible to use radial artery as free graft in myocardial revascularization?

El trabajo consiste en el estudio anatomofuncional de los arcos palmares a través del eco-Doppler. Para ello se realizaron pruebas funcionales como el ejercicio de la mano con clampeo radial, tomando imágenes pre y posclampeo para verificar el sentido del flujo, como así también semidieron las velocidades pre, intra y posclampeo. Durante las pruebas mencionadas se registró la oximetría de pulso en el dedo índice de la mano evaluada. Se halló que en el 90,5 por ciento de los pacientes estudiados se puede realizar la exéresis de la arteria radial sin que ocurran complicaciones isquémicas, confirmando así la validez del presente análisis (AU)