Identification of Terrisporobacter muris isolated from human blood using whole-genome sequencing: A case report.

We describe a case of Terrisporobacter muris bacteremia in a critically ill elderly man with a sigmoid colonic perforation. Initially, MALDI-TOF MS identified the bacterium as T. glycolicus; however, 16S rRNA gene sequencing suggested the presence of T. glycolicus, T. mayombei, or T. petrolearius. Owing to the limitations of these methods in distinguishing among the Terrisporobacter species, we employed whole-genome sequencing, which revealed the involvement of T. muris in the infection. This case represents the second report of T. muris isolated from human blood culture and is the first to describe the clinical course of this infection. Our findings underscore the diagnostic utility of whole-genome sequencing in accurately identifying novel infectious agents.

Tackling prediction uncertainty in machine learning for healthcare.

Predictive machine-learning systems often do not convey the degree of confidence in the correctness of their outputs. To prevent unsafe prediction failures from machine-learning models, the users of the systems should be aware of the general accuracy of the model and understand the degree of confidence in each individual prediction. In this Perspective, we convey the need of prediction-uncertainty metrics in healthcare applications, with a focus on radiology. We outline the sources of prediction uncertainty, discuss how to implement prediction-uncertainty metrics in applications that require zero tolerance to errors and in applications that are error-tolerant, and provide a concise framework for understanding prediction uncertainty in healthcare contexts. For machine-learning-enabled automation to substantially impact healthcare, machine-learning models with zero tolerance for false-positive or false-negative errors must be developed intentionally.

Prevalence and Risk Factors of Germline Pathogenic Variants in Pancreatic Ductal Adenocarcinoma.

PURPOSE: The genetic attribution for pancreatic ductal adenocarcinoma (PDAC) has been reported as 5%-10%. However, the incidence of germline pathogenic variants (PVs) in Korean PDAC patients has not been thoroughly investigated. Therefore, we studied to identify the risk factors and prevalence of PV for future treatment strategies in PDAC. MATERIALS AND METHODS: Total of 300 (155 male) patients with a median age of 65 years (range, 33 to 90 years) were enrolled in National Cancer Center in Korea. Cancer predisposition genes, clinicopathologic characteristics, and family history of cancer were analyzed. RESULTS: PVs were detected in 20 patients (6.7%, median age 65) in ATM (n=7, 31.8%), BRCA1 (n=3, 13.6%), BRCA2 (n=3), and RAD51D (n=3). Each one patient showed TP53, PALB2, PMS2, RAD50, MSH3, and SPINK1 PV. Among them, two likely PVs were in ATM and RAD51D, respectively. Family history of various types of cancer including pancreatic cancer (n=4) were found in 12 patients. Three patients with ATM PVs and a patient with three germline PVs (BRCA2, MSH3, and RAD51D) had first-degree relatives with pancreatic cancer. Familial pancreatic cancer history and PVs detection had a significant association (4/20, 20% vs. 16/264, 5.7%; p=0.035). CONCLUSION: Our study demonstrated that germline PVs in ATM, BRCA1, BRCA2, and RAD51D are most frequent in Korean PDAC patients and it is comparable to those of different ethnic groups. Although this study did not show guidelines for germline predisposition gene testing in patients with PDAC in Korea, it would be emphasized the need for germline testing for all PDAC patients.

Evaluation of Multiplex Rapid Antigen Tests for the Simultaneous Detection of SARS-CoV-2 and Influenza A/B Viruses.

Single-target rapid antigen tests (RATs) are commonly used to detect highly transmissible respiratory viruses (RVs), such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. The simultaneous detection of RVs presenting overlapping symptoms is vital in making appropriate decisions about treatment, isolation, and resource utilization; however, few studies have evaluated multiplex RATs for SARS-CoV-2 and other RVs. We assessed the diagnostic performance of multiplex RATs targeting both the SARS-CoV-2 and influenza A/B viruses with the GenBody Influenza/COVID-19 Ag Triple, InstaView COVID-19/Flu Ag Combo (InstaView), STANDARDTM Q COVID-19 Ag Test, and STANDARDTM Q Influenza A/B Test kits using 974 nasopharyngeal swab samples. The cycle threshold values obtained from the real-time reverse transcription polymerase chain reaction results showed higher sensitivity (72.7-100%) when the values were below, rather than above, the cut-off values. The InstaView kit exhibited significantly higher positivity rates (80.21% for SARS-CoV-2, 61.75% for influenza A, and 46.15% for influenza B) and cut-off values (25.57 for SARS-CoV-2, 21.19 for influenza A, and 22.35 for influenza B) than the other two kits, and was able to detect SARS-CoV-2 Omicron subvariants. Therefore, the InstaView kit is the best choice for routine screening for both SARS-CoV-2 and influenza A/B in local communities.

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).

BACKGROUND: Although copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of their clinical significance requires a thorough literature review. This process can be extremely time-consuming and may delay disease diagnosis. Therefore, we have developed CNV Extraction, Transformation, and Loading Artificial Intelligence (CNV-ETLAI), an innovative tool that allows experts to classify and interpret CNVs accurately and efficiently. METHODS: We combined text, table, and image processing algorithms to develop an artificial intelligence platform that automatically extracts, transforms, and organizes CNV information into a database. To validate CNV-ETLAI, we compared its performance to ground truth datasets labeled by a human expert. In addition, we analyzed the CNV data, which was collected using CNV-ETLAI via a crowdsourcing approach. RESULTS: In comparison to a human expert, CNV-ETLAI improved CNV detection accuracy by 4% and performed the analysis 60 times faster. This performance can improve even further with upscaling of the CNV-ETLAI database as usage increases. 5,800 CNVs from 2,313 journal articles were collected. Total CNV frequency for the whole chromosome was highest for chromosome X, whereas CNV frequency per 1 Mb of genomic length was highest for chromosome 22. CONCLUSIONS: We have developed, tested, and shared CNV-ETLAI for research and clinical purposes (https://lmic.mgh.harvard.edu/CNV-ETLAI). Use of CNV-ETLAI is expected to ease and accelerate diagnostic classification and interpretation of CNVs.

Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application.

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.

The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such public databases; one approach, for example, focused on labor-intensive, manual labeling of subsets of these datasets to be used to train new models. In this study, we describe a method for standardized, automated labeling based on similarity to a previously validated, explainable AI (xAI) model-derived-atlas, for which the user can specify a quantitative threshold for a desired level of accuracy (the probability-of-similarity, pSim metric). We show that our xAI model, by calculating the pSim values for each clinical output label based on comparison to its training-set derived reference atlas, can automatically label the external datasets to a user-selected, high level of accuracy, equaling or exceeding that of human experts. We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.

MarkIt: A Collaborative Artificial Intelligence Annotation Platform Leveraging Blockchain For Medical Imaging Research.

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.