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Dermatology Dermatology

Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care.

In Scientific reports ; h5-index 158.0

Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.

Escalé-Besa Anna, Yélamos Oriol, Vidal-Alaball Josep, Fuster-Casanovas Aïna, Miró Catalina Queralt, Börve Alexander, Ander-Egg Aguilar Ricardo, Fustà-Novell Xavier, Cubiró Xavier, Rafat Mireia Esquius, López-Sanchez Cristina, Marin-Gomez Francesc X

2023-Mar-15

General General

Hybrid Classic-Quantum Computing for Staging of Invasive Ductal Carcinoma of Breast

ArXiv Preprint

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum perspective, this article deals with the application of quantum computing techniques for the staging of Invasive Ductal Carcinoma of the breast. It includes: (1) a general explanation of a classical, and well-established, approach for medical reasoning, (2) a description of the clinical problem, (3) a conceptual model for staging invasive ductal carcinoma, (4) some basic notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the proposed approach for quantum staging of the invasive ductal carcinoma, and (6) the results obtained after running the quantum system on a significant number of use cases. A detailed discussion is also provided at the end of this paper.

Vicente Moret-Bonillo, Eduardo Mosqueira-Rey, Samuel Magaz-Romero, Diego Alvarez-Estevez

2023-03-17

General General

IntelliSleepScorer, a software package with a graphic user interface for automated sleep stage scoring in mice based on a light gradient boosting machine algorithm.

In Scientific reports ; h5-index 158.0

Machine learning has been applied in recent years to categorize sleep stages (NREM, REM, and wake) using electroencephalogram (EEG) recordings; however, a well-validated sleep scoring automatic pipeline in rodent research is still not publicly available. Here, we present IntelliSleepScorer, a software package with a graphic user interface to score sleep stages automatically in mice. IntelliSleepScorer uses the light gradient boosting machine (LightGBM) to score sleep stages for each epoch of recordings. We developed LightGBM models using a large cohort of data, which consisted of 5776 h of sleep EEG and electromyogram (EMG) signals across 519 unique recordings from 124 mice. The LightGBM model achieved an overall accuracy of 95.2% and a Cohen's kappa of 0.91, which outperforms the baseline models such as the logistic regression model (accuracy = 93.3%, kappa = 0.88) and the random forest model (accuracy = 94.3%, kappa = 0.89). The overall performance of the LightGBM model as well as the performance across different sleep stages are on par with that of the human experts. Most importantly, we validated the generalizability of the LightGBM models: (1) The LightGBM model performed well on two publicly available, independent datasets (kappa >  = 0.80), which have different sampling frequency and epoch lengths; (2) The LightGBM model performed well on data recorded at a lower sampling frequency (kappa = 0.90); (3) The performance of the LightGBM model is not affected by the light/dark cycle; and (4) A modified LightGBM model performed well on data containing only one EEG and one EMG electrode (kappa >  = 0.89). Taken together, the LightGBM models offer state-of-the-art performance for automatic sleep stage scoring in mice. Last, we implemented the IntelliSleepScorer software package based on the validated model to provide an out-of-box solution to sleep researchers (available for download at https://sites.broadinstitute.org/pan-lab/resources ).

Wang Lei A, Kern Ryan, Yu Eunah, Choi Soonwook, Pan Jen Q

2023-Mar-15

Internal Medicine Internal Medicine

The use of Smart Environments and Robots for Infection Prevention Control: a systematic literature review.

In American journal of infection control ; h5-index 43.0

Infection prevention and control (IPC) is essential to prevent nosocomial infections. The implementation of automation technologies can aid outbreak response. This manuscript aims at investigating the current use and role of robots and smart environments on IPC systems in nosocomial settings. The systematic literature review was performed following the PRISMA statement. Literature was searched for articles published in the period January 2016 to October 2022. Two authors determined the eligibility of the papers, with conflicting decisions being mitigated by a third. Relevant data was then extracted using an ad-hoc extraction table to facilitate the analysis and narrative synthesis. The quality of the included studies was appraised by two authors. The search strategy returned 1520 citations and 17 papers were included in this review. This review identified three main areas of interest: hand hygiene and personal protective equipment compliance, automatic infection cluster detection and environments cleaning (i.e., air quality control, sterilization). This review demonstrates that IPC practices within hospitals mostly do not rely on automation and robotic technology, and few advancements have been made in this field. Increasing the awareness of health care workers on these technologies, through training and involving them in the design process, is essential to accomplish the Health 4.0 transformation. Research priorities should also be considering how to implement similar or more contextualized alternatives for low-income countries.

Piaggio Davide, Zarro Marianna, Pagliara Silvio, Andellini Martina, Almuhini Abdulaziz, Maccaro Alessia, Pecchia Leandro

2023-Mar-14

Infection prevention and control, artificial intelligence, hand hygiene, health 4.0, internet of things, robot

General General

Causal Discovery from Temporal Data: An Overview and New Perspectives

ArXiv Preprint

Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data casual discovery.

Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi

2023-03-17

General General

Distributed output formation tracking control of heterogeneous multi-agent systems using reinforcement learning.

In ISA transactions

This paper studies the distributed time-varying output formation tracking problem for heterogeneous multi-agent systems with both diverse dimensions and parameters. The output of each follower is supposed to track that of the virtual leader while accomplishing a time-varying formation configuration. First, a distributed trajectory generator is proposed based on neighboring interactions to reconstitute the state of virtual leader and provide expected trajectories with the formation incorporated. Second, an optimal tracking controller is designed by the model-free reinforcement learning technique using online off-policy data instead of requiring any knowledge of the followers' dynamics. Stabilities of the learning process and resulting controller are analyzed while solutions to the output regulator equations are equivalently obtained. Third, a compensational input is designed for each follower based on previous learning results and a derived feasibility condition. It is proved that the output formation tracking error converges to zero asymptotically with the biases to cost functions being restricted arbitrarily small. Finally, numerical simulations verify the proposed learning and control scheme.

Shi Yu, Dong Xiwang, Hua Yongzhao, Yu Jianglong, Ren Zhang

2023-Mar-08

Distributed trajectory generator, Heterogeneous system, Output formation tracking, Reinforcement learning