Prognostication of patients with acute disorders of consciousness is imprecise but more accurate technology-supported predictions, such as cognitive motor dissociation (CMD), are emerging. CMD refers to the detection of willful brain activation following motor commands using functional magnetic resonance imaging or machine learning-supported analysis of the electroencephalogram in clinically unresponsive patients. CMD is associated with long-term recovery, but acceptance by surrogates and health care professionals is uncertain. The objective of this study was to determine receptiveness for CMD to inform goals of care (GoC) decisions and research participation among health care professionals and surrogates of behaviorally unresponsive patients. This was a two-center study of surrogates of and health care professionals caring for unconscious patients with severe neurological injury who were enrolled in two prospective US-based studies. Participants completed a 13-item survey to assess demographics, religiosity, minimal acceptable level of recovery, enthusiasm for research participation, and receptiveness for CMD to support GoC decisions. Completed surveys were obtained from 196 participants (133 health care professionals and 63 surrogates). Across all respondents, 93% indicated that they would want their loved one or the patient they cared for to participate in a research study that supports recovery of consciousness if CMD were detected, compared to 58% if CMD were not detected. Health care professionals were more likely than surrogates to change GoC with a positive (78% vs. 59%, p = 0.005) or negative (83% vs. 59%, p = 0.0002) CMD result. Participants who reported religion was the most important part of their life were least likely to change GoC with or without CMD. Participants who identified as Black (odds ratio [OR] 0.12, 95% confidence interval [CI] 0.04-0.36) or Hispanic/Latino (OR 0.39, 95% CI 0.2-0.75) and those for whom religion was the most important part of their life (OR 0.18, 95% CI 0.05-0.64) were more likely to accept a lower minimum level of recovery. Technology-supported prognostication and enthusiasm for clinical trial participation was supported across a diverse spectrum of health care professionals and surrogate decision-makers. Education for surrogates and health care professionals should accompany integration of technology-supported prognostication.

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stands in stark contrast with clinician decision-making which employs diverse information from multiple sources. While several multimodal machine learning approaches exist, significant challenges in developing multimodal systems remain that are hindering clinical adoption. In this paper, we introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies. In an illustrative application using a multimodal skin lesion dataset, we highlight the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning. We have open-sourced our framework as a tool for the community and hope it will accelerate the uptake of multimodal machine learning in healthcare and spur further innovation.

Identifying patients likely to regain consciousness early on is a challenge. The assessment of consciousness levels and the prediction of wakefulness probabilities are facilitated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET). This study aimed to develop a prognostic model for predicting 1-year postinjury outcomes in prolonged disorders of consciousness (DoC) using ¹⁸F-FDG PET alongside clinical behavioral scores. Eighty-seven patients with prolonged DoC newly diagnosed with behavioral Coma Recovery Scale-Revised (CRS-R) scores and ¹⁸F-FDG PET/computed tomography (18F-FDG PET/CT) scans were included. PET images were normalized by the cerebellum and extracerebral tissue, respectively. Images were divided into training and independent test sets at a ratio of 5:1. Image-based classification was conducted using the DenseNet121 network, whereas tabular-based deep learning was employed to train depth features extracted from imaging models and behavioral CRS-R scores. The performance of the models was assessed and compared using the McNemar test. Among the 87 patients with DoC who received routine treatments, 52 patients showed recovery of consciousness, whereas 35 did not. The classification of the standardized uptake value ratio by extracerebral tissue model demonstrated a higher specificity and lower sensitivity in predicting consciousness recovery than the classification of the standardized uptake value ratio by cerebellum model. With area under the curve values of 0.751 ± 0.093 and 0.412 ± 0.104 on the test sets, respectively, the difference is not statistically significant (P = 0.73). The combination of standardized uptake value ratio by extracerebral tissue and computed tomography depth features with behavioral CRS-R scores yielded the highest classification accuracy, with area under the curve values of 0.950 ± 0.027 and 0.933 ± 0.015 on the training and test sets, respectively, outperforming any individual mode. In this preliminary study, a multimodal prognostic model based on ¹⁸F-FDG PET extracerebral normalization and behavioral CRS-R scores facilitated the prediction of recovery in DoC.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.

Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some of these nodules may undergo fine-needle aspiration (FNA) biopsy to assess their nature, this procedure carries a risk of false negatives and inherent complications. To avoid the need for unnecessary biopsy examination, we explored a method for distinguishing the benign and malignant characteristics of thyroid TI-RADS 3-5 nodules based on deep-learning ultrasound images combined with computed tomography (CT). Thyroid nodules, assessed as American College of Radiology (ACR) TI-RADS category 3-5 through conventional ultrasound, all of which had postoperative pathology results, were examined using both conventional ultrasound and CT before operation. We investigated the effectiveness of deep-learning models based on ultrasound alone, CT alone, and a combination of both imaging modalities using the following metrics: Area Under Curve (AUC), sensitivity, accuracy, and positive predictive value (PPV). Additionally, we compared the diagnostic efficacy of the combined methods with manual readings of ultrasound and CT. A total of 768 thyroid nodules falling within TI-RADS categories 3-5 were identified across 768 patients. The dataset comprised 499 malignant and 269 benign cases. For the automatic identification of thyroid TI-RADS category 3-5 nodules, deep learning combined with ultrasound and CT demonstrated a significantly higher AUC (0.930; 95% CI: 0.892, 0.969) compared to the application of ultrasound alone AUC (0.901; 95% CI: 0.856, 0.947) or CT alone AUC (0.776; 95% CI: 0.713, 0.840). Additionally, the AUC of combined modalities surpassed that of radiologists'assessments using ultrasound alone AUCmean (0.725；95% CI:0.677, 0.773), CT alone AUCmean (0.617; 95% CI:0.564, 0.669). Deep learning method combined with ultrasound and CT imaging of thyroid can allow more accurate and precise classification of nodules within TI-RADS categories 3-5.

Integrating artificial intelligence (AI) in the dental field can potentially enhance the efficiency of dental care. However, few studies have investigated whether AI software can achieve results comparable to those obtained by dental practitioners (general practitioners (GPs) and specialists) when assessing alveolar bone loss in a clinical setting. Thus, this study compared the performance of AI in assessing periodontal bone loss with those of GPs and specialists. This comparative cross-sectional study evaluated the performance of dental practitioners and AI software in assessing alveolar bone loss. Radiographs were randomly selected to ensure representative samples. Dental practitioners independently evaluated the radiographs, and the AI software "Second Opinion Software" was tested using the same set of radiographs evaluated by the dental practitioners. The results produced by the AI software were then compared with the baseline values to measure their accuracy and allow direct comparison with the performance of human specialists. The survey received 149 responses, where each answered 10 questions to compare the measurements made by AI and dental practitioners when assessing the amount of bone loss radiographically. The mean estimates of the participants had a moderate positive correlation with the radiographic measurements (rho = 0.547, <i>p</i> < 0.001) and a weaker but still significant correlation with AI measurements (rho = 0.365, <i>p</i> < 0.001). AI measurements had a stronger positive correlation with the radiographic measurements (rho = 0.712, <i>p</i> < 0.001) compared with their correlation with the estimates of dental practitioners. This study highlights the capacity of AI software to enhance the accuracy and efficiency of radiograph-based evaluations of alveolar bone loss. Dental practitioners are vital for the clinical experience but AI technology provides a consistent and replicable methodology. Future collaborations between AI experts, researchers, and practitioners could potentially optimize patient care.

This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules. Conducted from March to December 2022, internal medicine residents underwent three repeated learning sessions with a "learning set" comprising 3000 thyroid nodule images. Diagnostic performances for internal medicine residents were assessed before the study, after every learning session, and for radiology residents before and after one-on-one education, using a "test set," comprising 120 thyroid nodule images. Finally, all residents repeated the same test using artificial intelligence computer-assisted diagnosis (AI-CAD). Twenty-one internal medicine and eight radiology residents participated. Initially, internal medicine residents had a lower area under the receiver operating characteristic curve (AUROC) than radiology residents (0.578 vs. 0.701, P < 0.001), improving post-learning (0.578 to 0.709, P < 0.001) to a comparable level with radiology residents (0.709 vs. 0.735, P = 0.17). Further improvement occurred with AI-CAD for both group (0.709 to 0.755, P < 0.001; 0.735 to 0.768, P = 0.03). The proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners, such as residents, in thyroid imaging to differentiate benign and malignant thyroid nodules. Additionally, AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.

Availability of ultraportable chest x-ray (CXR) and advancements in artificial intelligence (AI)-enabled CXR interpretation are promising developments in tuberculosis (TB) active case finding (ACF) but costing and cost-effectiveness analyses are limited. We provide implementation cost and cost-effectiveness estimates of different screening algorithms using symptoms, CXR and AI in Nigeria. People 15 years and older were screened for TB symptoms and offered a CXR with AI-enabled interpretation using qXR v3 (Qure.ai) at lung health camps. Sputum samples were tested on Xpert MTB/RIF for individuals reporting symptoms or with qXR abnormality scores ≥0.30. We conducted a retrospective costing using a combination of top-down and bottom-up approaches while utilizing itemized expense data from a health system perspective. We estimated costs in five screening scenarios: abnormality score ≥0.30 and ≥0.50; cough ≥ 2 weeks; any symptom; abnormality score ≥0.30 or any symptom. We calculated total implementation costs, cost per bacteriologically-confirmed case detected, and assessed cost-effectiveness using incremental cost-effectiveness ratio (ICER) as additional cost per additional case. Overall, 3205 people with presumptive TB were identified, 1021 were tested, and 85 people with bacteriologically-confirmed TB were detected. Abnormality ≥ 0.30 or any symptom (US$65704) had the highest costs while cough ≥ 2 weeks was the lowest (US$40740). The cost per case was US$1198 for cough ≥ 2 weeks, and lowest for any symptom (US$635). Compared to baseline strategy of cough ≥ 2 weeks, the ICER for any symptom was US$191 per additional case detected and US$ 2096 for Abnormality ≥0.30 OR any symptom algorithm. Using CXR and AI had lower cost per case detected than any symptom screening criteria when asymptomatic TB was higher than 30% of all bacteriologically-confirmed TB detected. Compared to traditional symptom screening, using CXR and AI in combination with symptoms detects more cases at lower cost per case detected and is cost-effective. TB programs should explore adoption of CXR and AI for screening in ACF.

Current anomaly detection methods excel with benchmark industrial data but struggle with natural images and medical data due to varying definitions of 'normal' and 'abnormal.' This makes accurate identification of deviations in these fields particularly challenging. Especially for 3D brain MRI data, all the state-of-the-art models are reconstruction-based with 3D convolutional neural networks which are memory-intensive, time-consuming and producing noisy outputs that require further post-processing. We propose a framework called Simple Slice-based Network (SimpleSliceNet), which utilizes a model pre-trained on ImageNet and fine-tuned on a separate MRI dataset as a 2D slice feature extractor to reduce computational cost. We aggregate the extracted features to perform anomaly detection tasks on 3D brain MRI volumes. Our model integrates a conditional normalizing flow to calculate log likelihood of features and employs the contrastive loss to enhance anomaly detection accuracy. The results indicate improved performance, showcasing our model's remarkable adaptability and effectiveness when addressing the challenges exists in brain MRI data. In addition, for the large-scale 3D brain volumes, our model SimpleSliceNet outperforms the state-of-the-art 2D and 3D models in terms of accuracy, memory usage and time consumption. Code is available at: https://github.com/Jarvisarmy/SimpleSliceNet.