Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer probabilities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce a generalised label-free framework, GenClean, for real-time artefact cleaning and leverage an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training. We first investigate patient-level generalisation, demonstrating robust performances under both intra- and inter-patient distribution shifts. We further validate its effectiveness through challenging cross-disease cohort experiments on the MIMIC-III database. Additionally, we extend our method to photoplethysmography (PPG), highlighting its applicability to diverse medical pulsatile signals. Finally, its integration into ICM+, a clinical research monitoring software, confirms the real-time feasibility of our framework, emphasising its practical utility in continuous physiological monitoring. This work provides a foundational step toward precision medicine in improving the reliability of high-resolution medical time series analysis

This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) and Truncated-Uniform-Laplace (TuLap) mechanisms within the framework of differential privacy (DP). We view stratified sampling as a subsampling operation, which amplifies the privacy guarantee; however, to have the same final privacy guarantee for each group, different nominal privacy budgets need to be used depending on the subsampling rate. Ignoring the effect of DP, traditional stratified sampling strategies risk significant variance inflation. We phrase our optimal survey design as an optimization problem, where we determine the optimal subsampling sizes for each group with the goal of minimizing the variance of the resulting estimator. We establish strong convexity of the variance objective, propose an efficient algorithm to identify the integer-optimal design, and offer insights on the structure of the optimal design.

The field of artificial intelligence (AI) is rapidly influencing health and healthcare, but bias and poor performance persists for populations who face widespread structural oppression. Previous work has clearly outlined the need for more rigorous attention to data representativeness and model performance to advance equity and reduce bias. However, there is an opportunity to also improve the explainability of AI by leveraging best practices of social epidemiology and health equity to help us develop hypotheses for associations found. In this paper, we focus on explainable AI (XAI) and describe a framework for interdisciplinary expert panel review to discuss and critically assess AI model explanations from multiple perspectives and identify areas of bias and directions for future research. We emphasize the importance of the interdisciplinary expert panel to produce more accurate, equitable interpretations which are historically and contextually informed. Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature. In turn, these insights can suggest opportunities for AI model improvement.

AI models have shown promise in many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust in AI-based models, and could enable novel scientific discovery by uncovering signals in the data that are not yet known to experts. In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task. We propose the following 4 steps: (i) Train a classifier to perform a given task (ii) Train a classifier guided StyleGAN-based image generator (StylEx) (iii) Automatically detect and visualize the top visual attributes that the classifier is sensitive towards (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, we present the discovered attributes to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health. We demonstrate results on eight prediction tasks across three medical imaging modalities: retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples of attributes that capture clinically known features, confounders that arise from factors beyond physiological mechanisms, and reveal a number of physiologically plausible novel attributes. Our approach has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models. Importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors. Finally, we intend to release code to enable researchers to train their own StylEx models and analyze their predictive tasks.

External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about additional systemic medical conditions. We developed a deep learning system (DLS) that takes external eye photos as input and predicts multiple systemic parameters, such as those related to the liver (albumin, AST); kidney (eGFR estimated using the race-free 2021 CKD-EPI creatinine equation, the urine ACR); bone & mineral (calcium); thyroid (TSH); and blood count (Hgb, WBC, platelets). Development leveraged 151,237 images from 49,015 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA. Evaluation focused on 9 pre-specified systemic parameters and leveraged 3 validation sets (A, B, C) spanning 28,869 patients with and without diabetes undergoing eye screening in 3 independent sites in Los Angeles County, CA, and the greater Atlanta area, GA. We compared against baseline models incorporating available clinicodemographic variables (e.g. age, sex, race/ethnicity, years with diabetes). Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST>36, calcium<8.6, eGFR<60, Hgb<11, platelets<150, ACR>=300, and WBC<4 on validation set A (a patient population similar to the development sets), where the AUC of DLS exceeded that of the baseline by 5.2-19.4%. On validation sets B and C, with substantial patient population differences compared to the development sets, the DLS outperformed the baseline for ACR>=300 and Hgb<11 by 7.3-13.2%. Our findings provide further evidence that external eye photos contain important biomarkers of systemic health spanning multiple organ systems. Further work is needed to investigate whether and how these biomarkers can be translated into clinical impact.

DEERFIELD, Ill., Jan. 18, 2022 (GLOBE NEWSWIRE) -- Surgalign Holdings, Inc., (NASDAQ: SRGA) a global medical technology company focused on elevating the standard of care by driving the evolution of digital health, today announced that it has received U.S. Food & Drug Administration (FDA) 510(k) clearance for its HOLO Portal surgical guidance system for use within lumbar spine procedures. The HOLO Portal system is the world's first artificial intelligence (AI)-driven augmented reality (AR) guidance system for spine and the first clinical application of Surgalign's HOLOTM AI digital health platform. "Receiving the initial clearance for the HOLO Portal system is a significant milestone and represents a critical step toward building the foundation of the digital surgery of the future. This system is designed to improve patient outcomes by delivering intelligent solutions to our customers, and we believe it is truly revolutionary," said Terry Rich, Surgalign's president and chief executive officer. "With clearance in hand for our guidance application, our near-term focus is getting the platform into the hands of surgeons as we work towards a market release. While the current capabilities of the HOLO Portal system have the potential to offer a quantum leap in the way surgical procedures are performed, we have a much larger vision for our HOLO AI digital health platform across a variety of healthcare specialties and throughout the care continuum."

The machine learning system is part of HOLO AITM, Surgalign's core technology in artificial intelligence and augmented reality. DEERFIELD, Ill., Aug. 19, 2021 – Surgalign Holdings, Inc., (NASDAQ: SRGA) a global medical technology company focused on elevating the standard of care by driving the evolution of digital surgery, today announced that the United States Patent and Trademark Office (USPTO) recently issued a patent covering a machine learning system for automated segmentation of a three-dimensional bony structure in a medical image. The granted patent expands and further strengthens the company's HOLO AI technology portfolio. "This patent is a foundational element of how we harness technology and data to power our digital surgery platform," said Terry Rich, Surgalign's president and chief executive officer. "While'artificial intelligence' and'machine learning' have become buzzwords that are often misused, misrepresented, and misunderstood, at Surgalign AI is a core competency and a key element of our efficient and highly valuable approach to improving patient's lives."

DEERFIELD, Ill., June 07, 2021 (GLOBE NEWSWIRE) -- Surgalign Holdings, Inc. (NASDAQ: SRGA), a global medical technology company focused on elevating the standard of care through the evolution of digital surgery, and Inteneural Networks Inc., a developer of innovative artificial intelligence (AI) based applications focused on fully autonomous analytics of central nervous system imaging, today announced that they have entered into a strategic collaboration agreement. Under the agreement, Surgalign will gain access to Inteneural's proprietary technology for evaluation of future integration within the Surgalign digital surgery portfolio. Inteneural is the developer and owner of proprietary intellectual property that allows computers to autonomously segment and identify neural structures in medical images and rapidly deliver reference information using machine learning alogrithims. These algorithms have potential for future applications in cranial and neurosurgery for referencing of tumor, aneurysm, stroke, and neurovascular structures using existing magnetic resonance imaging and computed tomography technology. "While our initial focus is the application of digital surgery in spine procedures, we have a much more expansive vision for what we believe is possible with emerging technologies. New developments in the application of AI in neurosurgery and medical imaging make it an attractive space to further expand. Inteneural has developed machine learning-based analytics and fully autonomous brain anatomy segmentation capabilities that would be incredibly powerful when combined with neurosurgery," said Terry Rich, Surgalign Chief Executive Officer.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice

Several companies told Reuters the software will be crucial to staying open as concerns about COVID-19, the respiratory illness caused by the virus, persist around the world. It will allow them to show not only workers and customers, but also insurers and regulators, that they are monitoring and enforcing safe practices. "The last thing we want is for the governor to shut all our projects down because no one is behaving," said Jen Suerth, vice president at Chicago-based Pepper Construction, which introduced software from SmartVid.io Samarth Diamond plans to deploy AI from Glimpse Analytics as soon as its polishing factory re-opens in Gujarat, India, while two Michigan shopping centers owned by RPT Realty will have distancing tracking from RE Insight in two weeks. Buyers expect the technology will work because they already have used similar tools to profile shoppers entering stores and find helmet scofflaws on construction sites.