Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.

OBJECTIVE: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human electroencephalographic (EEG) recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci. METHODS: We analyzed 10 patients (aged 2.7-28.1 years) with medication-resistant focal epilepsy who underwent stereotactic EEG evaluation with thalamic sampling. We examined both undirected and directed connectivity, incorporating coherence and spectral Granger causality analysis (GCA) between the diverse seizure foci and thalamic nuclei (AN and CM) at ictal onset. RESULTS: In our analysis of 36 seizures, coherence between seizure onset and thalamic nuclei increased across all frequencies, especially in slower bands (delta, theta, alpha). GCA showed increased information flow from seizure onset to the thalamus across all frequency bands, but outflows from the thalamus were mainly in slower frequencies, particularly delta. In the subgroup analysis based on various seizure foci, the delta coherence showed a more pronounced increase at CM than at AN during frontal lobe seizures. Conversely, in limbic seizures, the delta coherence increase was greater at AN compared to CM. SIGNIFICANCE: It appears that the delta frequency plays a pivotal role in modulating the corticothalamic network during seizures. Our results underscore the significance of comprehending the spatiotemporal dynamics of the corticothalamic network at ictal onset, and this knowledge could guide personalized responsive neuromodulation treatment strategies.

Visualization of Fluoroscopic Imaging in Orthopedic Surgery: Head-Mounted Display vs Conventional Monitor.

Purpose. See-through head-mounted displays (HMDs) can be used to view fluoroscopic imaging during orthopedic surgical procedures. The goals of this study were to determine whether HMDs reduce procedure time, number of fluoroscopic images required, or number of head turns by the surgeon compared with standard monitors. Methods. Sixteen orthopedic surgery residents each performed fluoroscopy-guided drilling of 8 holes for placement of tibial nail distal interlocking screws in an anatomical model, with 4 holes drilled while using HMD and 4 holes drilled while using a standard monitor. Procedure time, number of fluoroscopic images needed, and number of head turns by the resident during the procedure were compared between the 2 modalities. Statistical significance was set at P < .05. Results. Mean (SD) procedure time did not differ significantly between attempts using the standard monitor (55 [37] seconds) vs the HMD (56 [31] seconds) (P = .73). Neither did mean number of fluoroscopic images differ significantly between attempts using the standard monitor vs the HMD (9 [5] images for each) (P = .84). Residents turned their heads significantly more times when using the standard monitor (9 [5] times) vs the HMD (1 [2] times) (P < .001). Conclusions. Head-mounted displays lessened the need for residents to turn their heads away from the surgical field while drilling holes for tibial nail distal interlocking screws in an anatomical model; however, there was no difference in terms of procedure time or number of fluoroscopic images needed using the HMD compared with the standard monitor.

Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer.

PURPOSE: The appropriate number of systematic biopsy cores to retrieve during magnetic resonance imaging (MRI)-targeted prostate biopsy is not well defined. We aimed to demonstrate a biopsy sampling approach that reduces required core count while maintaining diagnostic performance. MATERIALS AND METHODS: We collected data from a cohort of 971 men who underwent MRI-ultrasound fusion targeted biopsy for suspected prostate cancer. A regional targeted biopsy (RTB) was evaluated retrospectively; only cores within 2 cm of the margin of a radiologist-defined region of interest were considered part of the RTB. We compared detection rates for clinically significant prostate cancer (csPCa) and cancer upgrading rate on final whole mount pathology after prostatectomy between RTB, combined, MRI-targeted, and systematic biopsy. RESULTS: A total of 16,459 total cores from 971 men were included in the study data sets, of which 1,535 (9%) contained csPCa. The csPCa detection rates for systematic, MRI-targeted, combined, and RTB were 27.0% (262/971), 38.3% (372/971), 44.8% (435/971), and 44.0% (427/971), respectively. Combined biopsy detected significantly more csPCa than systematic and MRI-targeted biopsy (p <0.001 and p=0.004, respectively) but was similar to RTB (p=0.71), which used on average 3.8 (22%) fewer cores per patient. In 102 patients who underwent prostatectomy, there was no significant difference in upgrading rates between RTB and combined biopsy (p=0.84). CONCLUSIONS: A RTB approach can maintain state-of-the-art detection rates while requiring fewer retrieved cores. This result informs decision making about biopsy site selection and total retrieved core count.

Effect of altering breathing frequency on maximum voluntary ventilation in healthy adults.

BACKGROUND: Compared to other pulmonary function tests, there is a lack of standardization regarding how a maximum voluntary ventilation (MVV) maneuver is performed. Specifically, little is known about the variation in breathing frequency (fR) and its potential impact on the accuracy of test results. This study examines the effect of several preselected values for fR and one self-selected fR (fRself) on MVV. METHODS: Ten participants performed MVV maneuvers at various fR values, ranging from 50 to 130 breaths·min- 1 in 10 breaths·min- 1 intervals and at one fRself. Three identical trials with 2-min rest periods were conducted at each fR, and the sequence in which fR was tested was randomized. Ventilation and related parameters were measured directly by gas exchange analysis via a metabolic measurement system. RESULTS: A third-order polynomial regression analysis showed that MVV = - 0.0001(fR)3 + 0.0258(fR)2-1.38(fR) + 96.9 at preselected fR and increased up to approximately 100 breaths·min- 1 (r2 = 0.982, P < 0.001). Paired t-tests indicated that average MVV values obtained at all preselected fR values, but not fRself, were significantly lower than the average maximum value across all participants. A linear regression analysis revealed that tidal volume (VT) = - 2.63(MVV) + 300.4 at preselected fR (r2 = 0.846, P < 0.001); however, this inverse relationship between VT and MVV did not remain true for the self-selected fR. The VT obtained at this fR (90.9 ± 19.1% of maximum) was significantly greater than the VT associated with the most similar MVV value (at a preselected fR of 100 breaths·min- 1, 62.0 ± 10.4% of maximum; 95% confidence interval of difference: (17.5, 40.4%), P < 0.001). CONCLUSIONS: This study demonstrates the shortcomings of the current lack of standardization in MVV testing and establishes data-driven recommendations for optimal fR. The true MVV was obtained with a self-selected fR (mean ± SD: 69.9 ± 22.3 breaths·min- 1) or within a preselected fR range of 110-120 breaths·min- 1. Until a comprehensive reference equation is established, it is advised that MVV be measured directly using these guidelines. If an individual is unable to perform or performs the maneuver poorly at a self-selected fR, ventilating within a mandated fR range of 110-120 breaths·min- 1 may also be acceptable.

Using phrases and document metadata to improve topic modeling of clinical reports.

Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.

Patients' Perceptions of Radiation Exposure Associated With Mammography.

OBJECTIVE: Radiation associated with breast imaging is a sensitive issue, particularly for women who undergo mammography as a screening measure to detect breast cancer. Misinformation and misunderstanding regarding the risks associated with ionizing radiation have created heightened public concern and fear, which may result in avoidance of diagnostic procedures. The objectives of this study were to ascertain patients' knowledge and opinion of ionizing radiation as a whole and specifically in mammography, as well as to determine common misunderstandings and points of view that may affect women's decisions about whether to have a mammogram. MATERIALS AND METHODS: Over a 9-month period, a total of 1725 patients presenting for a mammogram completed a 25-point questionnaire focused on the following: general knowledge of radiation dose in common imaging modalities, the amount of radiation associated with a mammogram relative to five radiation benchmarks, and patients' opinions of the involvement of radiation in their health care. RESULTS: Although 65% of the women receiving a mammogram responded that they had been informed of the risks and benefits of the examination, 60% overestimated the radiation in a mammogram. CONCLUSION: Efforts should be made to accurately inform women of the risks and benefits of mammography, specifically highlighting the low dose of mammographic ionizing radiation and providing objective facts to ensure that they are making an informed decision regarding screening.

Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning.

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023 [1]. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.

Patients with Heart Failure: Internet Use and Mobile Health Perceptions.

BACKGROUND: Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders. OBJECTIVES: We sought to investigate three characteristics of heart failure patients with respect to their heart failure self-care behaviors: (1) internet use to search for heart failure information; (2) familiarity with mobile health apps and devices; and (3) perceptions of using activity trackers or smartwatches to aid in their heart failure self-care. METHODS: Forty-nine heart failure patients were asked about their internet and mobile health usage. The structured interview included questions adapted from the Health Information National Trends Survey. RESULTS: Over 50% of the patients had utilized the internet to search for heart failure information in the past 12 months, experience using health-related apps, and thoughts that an activity tracker or smartwatch could help them manage heart failure. Qualitative analysis of the interviews revealed six themes: trust in their physicians, alternatives to mobile health apps, lack of need for mobile health devices, financial barriers to activity tracker and smartwatch ownership, benefits of tracking and reminders, and uncertainty of their potential due to lack of knowledge. CONCLUSION: Trust in their physicians was a major factor for heart failure patients who reported not searching for health information on the internet. While those who used mobile health technologies found them useful, patients who did not use them were generally unaware of or unknowledgeable about them. Considering patients' preferences for recommendations from their physicians and tendency to search for heart failure information including treatment and management options, patient-provider discussions about mobile health may improve patient knowledge and impact their usage.

A multitask approach for automated detection and segmentation of thyroid nodules in ultrasound images.

An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9888 images, and annotated by collaborating radiologists. Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any UNet architecture variant to improve image-level nodule detection. Of the evaluated multitask models, a UNet with a ImageNet pretrained encoder and AD achieved the highest F1 score of 0.839 and image-wide Dice similarity coefficient of 0.808 on the hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging. MATERIALS AND METHODS: Our models were developed and evaluated using a cohort of 326 patients who underwent endovascular thrombectomy at UCLA Ronald Reagan Medical Center from 2014 to 2021. We designed a hybrid transformer model with nonlocal and cross-attention modules to predict the first-pass effect on MR imaging and CT series. RESULTS: The proposed method achieved a mean 0.8506 (SD, 0.0712) for cross-validation receiver operating characteristic area under the curve (ROC-AUC) on MR imaging and 0.8719 (SD, 0.0831) for cross-validation ROC-AUC on CT. When evaluated on the prospective test sets, our proposed model achieved a mean ROC-AUC of 0.7967 (SD, 0.0335) with a mean sensitivity of 0.7286 (SD, 0.1849) and specificity of 0.8462 (SD, 0.1216) for MR imaging and a mean ROC-AUC of 0.8051 (SD, 0.0377) with a mean sensitivity of 0.8615 (SD, 0.1131) and specificity 0.7500 (SD, 0.1054) for CT, respectively, representing the first classification of the first-pass effect from MR imaging alone and the first automated first-pass effect classification method in CT. CONCLUSIONS: Results illustrate that both nonperfusion MR imaging and CT from admission contain signals that can predict a successful first-pass effect following endovascular thrombectomy using our deep learning methods without requiring time-intensive manual segmentation.

From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.

CONTEXT: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice. EVIDENCE ACQUISITION: A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence. EVIDENCE SYNTHESIS: A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability. CONCLUSION: Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.

OBJECTIVES: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. MATERIALS AND METHODS: We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. RESULTS: We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity. DISCUSSION AND CONCLUSION: Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.

Updating annotations with the distributed annotation system and the automated sequence annotation pipeline.

SUMMARY: The integration between BioDAS ProServer and Automated Sequence Annotation Pipeline (ASAP) provides an interface for querying diverse annotation sources, chaining and linking results, and standardizing the output using the Distributed Annotation System (DAS) protocol. This interface allows pipeline plans in ASAP to be integrated into any system using HTTP and also allows the information returned by ASAP to be included in the DAS registry for use in any DAS-aware system. Three example implementations have been developed: the first accesses TRANSFAC information to automatically create gene sets for the Coordinated Gene Activity in Pattern Sets (CoGAPS) algorithm; the second integrates annotations from multiple array platforms and provides unified annotations in an R environment; and the third wraps the UniProt database for integration with the SPICE DAS client. AVAILABILITY: Source code for ASAP 2.7 and the DAS 1.6 interface is available under the GNU public license. Proserver 2.20 is free software available from SourceForge. Scripts for installation and configuration on Linux are provided at our website: http://www.rits.onc.jhmi.edu/dbb/custom/A6/

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.

Evaluating Deep Learning Performance for P300 Neural Signal Classification.

P300 event-related potential (ERP) signals are useful neurological biomarkers, and their accurate classification is important when studying the cognitive functions in patients with neurological disorders. While many studies have proposed models for classifying these signals, results have been inconsistent. As a result, a consensus has not yet been reached on the optimal model for this classification. In this study, we evaluated the performance of classic machine learning and novel deep learning methods for P300 signal classification in both within and across subject training scenarios across a dataset of 75 subjects. Although the deep learning models attained high attended event classification F1 scores, they did not outperform Stepwise Linear Discriminant Analysis (SWLDA) in the within-subject paradigm. In the across-subject paradigm, however, EEG-Inception was able to significantly outperform SWLDA. These results suggest that deep learning models may provide a general model that do not require subject-specific training and calibration in clinical settings.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

OBJECTIVE: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. METHODS: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. RESULTS: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. CONCLUSION AND SIGNIFICANCE: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.

A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images.

An increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification.Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any U-Net architecture variant to improve image-level nodule detection. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9,888 images, and annotated by collaborating radiologists. Of the evaluated models, a multi-scale UNet (MSUNet) with AD achieved the highest F1 score of 0.829 and image-wide Dice similarity coefficient of 0.782 on our hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study.

BACKGROUND: The increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise in lessening participation burden to provide actively contributed patient-reported outcome (PRO) information. OBJECTIVE: The aim of this study was to develop machine learning models to classify and predict PRO scores using Fitbit data from a cohort of patients with rheumatoid arthritis. METHODS: Two different models were built to classify PRO scores: a random forest classifier model that treated each week of observations independently when making weekly predictions of PRO scores, and a hidden Markov model that additionally took correlations between successive weeks into account. Analyses compared model evaluation metrics for (1) a binary task of distinguishing a normal PRO score from a severe PRO score and (2) a multiclass task of classifying a PRO score state for a given week. RESULTS: For both the binary and multiclass tasks, the hidden Markov model significantly (P<.05) outperformed the random forest model for all PRO scores, and the highest area under the curve, Pearson correlation coefficient, and Cohen κ coefficient were 0.750, 0.479, and 0.471, respectively. CONCLUSIONS: While further validation of our results and evaluation in a real-world setting remains, this study demonstrates the ability of physical activity tracker data to classify health status over time in patients with rheumatoid arthritis and enables the possibility of scheduling preventive clinical interventions as needed. If patient outcomes can be monitored in real time, there is potential to improve clinical care for patients with other chronic conditions.

Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency.

Introduction: Limbal Stem Cell Deficiency (LSCD) is a blinding corneal disease characterized by the loss of function or deficiency in adult stem cells located at the junction between the cornea and the sclera (i.e., the limbus), namely the limbal stem cells (LSCs). Recent advances in in vivo imaging technology have improved disease diagnosis and staging to quantify several biomarkers of in vivo LSC function including epithelial thickness measured by anterior segment optical coherence tomography, and basal epithelial cell density and subbasal nerve plexus by in vivo confocal microscopy. A decrease in central corneal sub-basal nerve density and nerve fiber and branching number has been shown to correlate with the severity of the disease in parallel with increased nerve tortuosity. Yet, image acquisition and manual quantification require a high level of expertise and are time-consuming. Manual quantification presents inevitable interobserver variability. Methods: The current study employs a novel deep learning approach to classify neuron morphology in various LSCD stages and healthy controls, by integrating images created through latent diffusion augmentation. The proposed model, a residual U-Net, is based in part on the InceptionResNetV2 transfer learning model. Results: Deep learning was able to determine fiber number, branching, and fiber length with high accuracy (R2 of 0.63, 0.63, and 0.80, respectively). The model trained on images generated through latent diffusion on average outperformed the same model when trained on solely original images. The model was also able to detect LSCD with an AUC of 0.867, which showed slightly higher performance compared to classification using manually assessed metrics. Discussion: The results suggest that utilizing latent diffusion to supplement training data may be effective in bolstering model performance. The results of the model emphasize the ability as well as the shortcomings of this novel deep learning approach to predict various nerve morphology metrics as well as LSCD disease severity.