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General General

Artificial intelligence in medicine creates real risk management and litigation issues.

In Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management

The next step in the evolution of electronic medical record (EMR) use is the integration of artificial intelligence (AI) into health care. With the benefit of roughly 15 years of electronic medical records (EMR) data from millions of patients, health systems can now leverage this historical information via the assistance of complex mathematical algorithms to formulate computer-based medical decisions. With AI spending in health care forecasted to increase from $2.1 billion currently to $36 billion by 2025,1 we sit on the precipice of the next revolution in health care. Now is the time to consider the potential risks, liability and litigation issues of using AI in health care.

Keris Matthew P


Cardiology Cardiology

Point-of-Care Ultrasound.

In Current cardiology reports

PURPOSE OF THE REVIEW : Point-of-care ultrasound using small ultrasound devices has expanded beyond emergency and critical care medicine to many other subspecialties. Awareness of the strengths and limitations of the technology and knowledge of the appropriate settings and common indications for point-of-care ultrasound is important.

RECENT FINDINGS : Point-of-care ultrasound is widely embraced as an extension of the physical exam and is employed in acute care and medical education settings. Echocardiography laboratories involved in education must individualize training to the intended scope of practice of the user. Advances in artificial intelligence may assist in image acquisition and interpretation by novice users. Point-of-care ultrasound is widely available in a variety of clinical settings. The field has advanced substantially in the past 2 decades and will likely continue to expand with advancement in technology, reduced cost, and improved opportunities to assist new users.

Lee Linda, DeCara Jeanne M


Cardiac, Echocardiography, Education, POCUS, Training, Ultrasound

General General

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality.

In Pharmaceutics

Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets.

Hirschberg Cosima, Edinger Magnus, Holmfred Else, Rantanen Jukka, Boetker Johan


artificial intelligence, image analysis, in silico modelling, multivariate analysis, neural networks

General General

Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

In Journal of thoracic disease ; h5-index 52.0

Background : Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented.

Methods : The subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener.

Results : The performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ≥15 events/hour of detected sleep time.

Conclusions : The "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).

Tiron Roxana, Lyon Graeme, Kilroy Hannah, Osman Ahmed, Kelly Nicola, O’Mahony Niall, Lopes Cesar, Coffey Sam, McMahon Stephen, Wren Michael, Conway Kieran, Fox Niall, Costello John, Shouldice Redmond, Lederer Katharina, Fietze Ingo, Penzel Thomas


Sleep-disordered breathing (SDB), apnea hypopnea index (AHI), obstructive sleep apnea (OSA), screening, smartphone

General General

An annotated data set for identifying women reporting adverse pregnancy outcomes on Twitter.

In Data in brief

Despite the prevalence in the United States of miscarriage [1], stillbirth [2], and infant mortality associated with preterm birth and low birthweight [3], their causes remain largely unknown [4], [5], [6]. To advance the use of social media data as a complementary resource for epidemiology of adverse pregnancy outcomes, we present a data set of 6487 tweets that mention miscarriage, stillbirth, preterm birth or premature labor, low birthweight, neonatal intensive care, or fetal/infant loss in general. These tweets are a subset of 22,912 tweets retrieved by applying hand-written regular expressions to a database containing more than 400 million public tweets posted by more than 100,000 women who have announced their pregnancy on Twitter [7]. Two professional annotators labeled the 6487 tweets in a binary fashion, distinguishing those potentially reporting that the user has personally experienced the outcome ("outcome" tweets) from those that merely mention the outcome ("non-outcome" tweets). Inter-annotator agreement was κ = 0.90 (Cohen's kappa). The tweets annotated as "outcome" include 1318 women reporting miscarriage, 94 stillbirth, 591 preterm birth or premature labor, 171 low birthweight, 453 neonatal intensive care, and 356 fetal/infant loss in general. These "outcome" tweets can be used to explore patient experiences and perceptions of adverse pregnancy outcomes, and can direct researchers to the users' broader timelines-tweets posted by a user over time-for observational studies. Our past work demonstrates the analysis of timelines for selecting a study population [8] and conducting a case-control study [9] of users reporting that their child has a birth defect. For larger-scale studies, the full annotated corpus can be used to train supervised machine learning algorithms to automatically identify additional users reporting adverse pregnancy outcomes on Twitter. We used the annotated corpus to train feature-engineered and deep learning-based classifiers presented in "A natural language processing pipeline to advance the use of Twitter data for digital epidemiology of adverse pregnancy outcomes" [10].

Klein Ari Z, Gonzalez-Hernandez Graciela


Data mining, Epidemiology, Machine learning, Natural language processing, Pregnancy, Social media

Ophthalmology Ophthalmology

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system.

In Eye and vision (London, England)

Background : The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule (A/V) ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.

Methods : The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head. Two fundus photos of a total of 19 eyes from 10 subjects were imaged. Retinal vessels were analyzed to obtain the A/V ratio. In addition, the vessel tree was extracted using deep learning U-NET, and vessel density was processed by the percentage of pixels within vessels over the entire image.

Results : All images were successfully processed for the A/V ratio and vessel density. There was no significant difference of averaged A/V ratio between the first (0.77 ± 0.09) and second (0.77 ± 0.10) measurements (P = 0.53). There was no significant difference of averaged vessel density (%) between the first (6.11 ± 1.39) and second (6.12 ± 1.40) measurements (P = 0.85).

Conclusions : Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope. The device appears to provide sufficient image quality for analyzing A/V ratio and vessel density with the benefit of portability, easy data transferring, and low cost of the device, which could be used for pre-clinical screening of systemic, cerebral and ocular diseases.

Hu Huiling, Wei Haicheng, Xiao Mingxia, Jiang Liqiong, Wang Huijuan, Jiang Hong, Rundek Tatjana, Wang Jianhua


Arteriovenous ratio, Deep learning, Image analysis, Retina, Smartphone ophthalmoscope, Vessel density