Deep convolutional neural network-based algorithm for muscle biopsy diagnosis.

Histopathologic evaluation of muscle biopsy samples is essential for classifying and diagnosing muscle diseases. However, the numbers of experienced specialists and pathologists are limited. Although new technologies such as artificial intelligence are expected to improve medical reach, their use with rare diseases, such as muscle diseases, is challenging because of the limited availability of training datasets. To address this gap, we developed an algorithm based on deep convolutional neural networks (CNNs) and collected 4041 microscopic images of 1400 hematoxylin-and-eosin-stained pathology slides stored in the National Center of Neurology and Psychiatry for training CNNs. Our trained algorithm differentiated idiopathic inflammatory myopathies (mostly treatable) from hereditary muscle diseases (mostly non-treatable) with an area under the curve (AUC) of 0.996 and achieved better sensitivity and specificity than the diagnoses done by nine physicians under limited diseases and conditions. Furthermore, it successfully and accurately classified four subtypes of the idiopathic inflammatory myopathies with an average AUC of 0.958 and classified seven subtypes of hereditary muscle disease with an average AUC of 0.936. We also established a method to validate the similarity between the predictions made by the algorithm and the seven physicians using visualization technology and clarified the validity of the predictions. These results support the reliability of the algorithm and suggest that our algorithm has the potential to be used straightforwardly in a clinical setting.

Prescription rate of erythropoietin-stimulating agents is low for patients with renal impairment under non-nephrology care in a tertiary-level academic medical center in Japan.

BACKGROUND: Erythropoiesis-stimulating agents (ESAs) and iron supplements may be prescribed appropriately under nephrology care. However, there are few reports detailing the differences in prescription rates of these therapies among clinical departments. METHODS: A total of 39,585 patients with renal impairment were enrolled from a database of 914,280 patients. Patients were selected based on an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m2. There were eight clinical departments from internal medicine, including nephrology. We defined a hemoglobin level less than 11.0 g/dL as anemia and set 20% of transferrin saturation and 100 ng/mL of serum ferritin as cutoff points. We compared the prescription rates of ESAs and iron supplementation based on the hemoglobin level and iron status among the patients seen across the eight clinical departments. RESULTS: The lower the eGFR, the more the number of patients seen under nephrology care. The rates of patients with no prescription were 52.3, 39.9, 45.9, and 54.3% among those with hemoglobin levels of < 8, 8 ≤ < 9, 9 ≤ < 10, and 10 ≤ < 11 g/dL, respectively. Of the patients with less than 11.0 g/dL of hemoglobin, 77.3% were prescribed ESAs under nephrology care. Meanwhile, only 18.5 and 8.2% of patients were prescribed ESAs in clinical departments of internal medicine, other than nephrology, and non-internal medicine care, respectively. CONCLUSION: Treatment for anemia has not been sufficiently performed in patients with renal impairment under non-nephrology care in a real-world clinical setting.

Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis.

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients. The extracted features were used to stratify a subpopulation of 3, 522 patients that showed anemia and were prescribed for cardiovascular-related drugs and progressed faster to dialysis. On the other side, clustering patients using conventional clustering methods based on their clinical features did not allow such clear interpretation to identify the main factors for leading fast progression to dialysis. To our knowledge this is the first study extracting interpretable features for stratifying a cohort of early CKD patients using time-to-event analysis which could help prevention and the development of new treatments.

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan.

OBJECTIVES: Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach. DESIGN: Retrospective single-centre cohort study. SETTINGS: Tertiary referral university hospital in Toyoake city, Japan. PARTICIPANTS: A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years. PRIMARY OUTCOME: Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters. RESULTS: Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics. CONCLUSIONS: The random forest model could be useful in identifying patients with extremely rapid eGFR decline. TRIAL REGISTRATION: UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data.

BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. METHODS: Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. FINDINGS: The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. INTERPRETATION: This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. FUNDING: IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: A machine learning-based prediction model by using a big database.

Artificial intelligence is increasingly being adopted in medical fields to predict various outcomes. In particular, chronic kidney disease (CKD) is problematic because it often progresses to end-stage kidney disease. However, the trajectories of kidney function depend on individual patients. In this study, we propose a machine learning-based model to predict the rapid decline in kidney function among CKD patients by using a big hospital database constructed from the information of 118,584 patients derived from the electronic medical records system. The database included the estimated glomerular filtration rate (eGFR) of each patient, recorded at least twice over a period of 90 days. The data of 19,894 patients (16.8%) were observed to satisfy the CKD criteria. We characterized the rapid decline of kidney function by a decline of 30% or more in the eGFR within a period of two years and classified the available patients into two groups-those exhibiting rapid eGFR decline and those exhibiting non-rapid eGFR decline. Following this, we constructed predictive models based on two machine learning algorithms. Longitudinal laboratory data including urine protein, blood pressure, and hemoglobin were used as covariates. We used longitudinal statistics with a baseline corresponding to 90-, 180-, and 360-day windows prior to the baseline point. The longitudinal statistics included the exponentially smoothed average (ESA), where the weight was defined to be 0.9*(t/b), where t denotes the number of days prior to the baseline point and b denotes the decay parameter. In this study, b was taken to be 7 (7-day ESA). We used logistic regression (LR) and random forest (RF) algorithms based on Python code with scikit-learn library (https://scikit-learn.org/) for model creation. The areas under the curve for LR and RF were 0.71 and 0.73, respectively. The 7-day ESA of urine protein ranked within the first two places in terms of importance according to both models. Further, other features related to urine protein were likely to rank higher than the rest. The LR and RF models revealed that the degree of urine protein, especially if it exhibited an increasing tendency, served as a prominent risk factor associated with rapid eGFR decline.