Jul 21,2023

Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes

This research aimed to investigate the impact of meal anticipation within a fully automated insulin delivery (AID) system on glycemic control in adults with type 1 diabetes (T1D). The study used a randomized crossover clinical trial with three AID system modalities: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Participants consumed meals at fixed times during supervised 24-hour admissions, and were started on a Tandem t:AP insulin pump set with their home insulin parameters and a Dexcom G6 CGM sensor session within 24–48 h before study start. Overall, the 24-hour glycemic control percentages for HCL, FCL, and FCL+ were 86%, 77%, and 77%, respectively. The study concluded that the inclusion of meal anticipation did not significantly enhance postprandial control within the AID systems, but it also did not increase the risk of low blood sugar when meals were delayed.

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#closed loop

#cgm

#insulin pump

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Aug 24,2023

Dario Publishes New Research Demonstrating Greater Engagemenet and Improved Clinical Outcomes Sustainable for Two Years

DarioHealth announced new research presented at the ADCES23 Annual Conference held earlier this month in Houston, Texas. The new research demonstrates Dario's ability to sustainably improve health outcomes for users with diabetes over a two-year period. The latest research analyzed the data of 119,482 Dario members to understand the relationship between improving engagement and health outcomes. The results showed that during the two years of data analyzed in the research study, users improved engagement 29% over two years. Users with high-risk diabetes demonstrated reductions in their high-blood glucose reading ratios and monthly average glucose that correlated with increased engagement.

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#mobile app

#dtx

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Aug 24,2023

Dario Publishes New Research Demonstrating Greater Engagemenet and Improved Clinical Outcomes Sustainable for Two Years

DarioHealth announced new research presented at the ADCES23 Annual Conference held earlier this month in Houston, Texas. The new research demonstrates Dario's ability to sustainably improve health outcomes for users with diabetes over a two-year period. The latest research analyzed the data of 119,482 Dario members to understand the relationship between improving engagement and health outcomes. The results showed that during the two years of data analyzed in the research study, users improved engagement 29% over two years. Users with high-risk diabetes demonstrated reductions in their high-blood glucose reading ratios and monthly average glucose that correlated with increased engagement.

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#mobile app

#coaching

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May 07,2020

Physiological data collected from Feel Program reveals negative emotions have almost doubled since lockdown

Feel Therapeutics has used objective data collected from physiological signals detected by their proprietary Feel Emotion Sensor, an emotion tracking wearable device, to understand and quantify the changes to the emotional states of a sample group of participants in Europe and the USA. Over the course of eight weeks, between February and April 2020, Feel collected data from a random sample group of participants in Europe and the USA, and analysed 128 million electro-dermal activity (EDA) samples, 400 million heart rate variability (HRV) samples, 64 million skin temperature (ST) samples, and approximately 900 significant emotional moments. Results from this recent study showed sudden and significant changes to those physiological signals, indicating that emotional changes or new emotions were being experienced.

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#connected device

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Jun 19,2021

Feel Therapeutics partners with academia & innovation experts in the HEART Project for Health & Well-being in Urban Environment

Feel Therapeutics, Inc., announced their participation in the HEART project, coordinated by the National Technical University of Athens (NTUA). The project kicked off with 18 partners from academia and different fields of expertise. The acronym HEART comes from the very philosophy of this urban-health and well-being inspired project’s title: HEAlthier cities Through blue-green Regenerative Technologies. The Feel Emotion Sensor will be used to monitor and assess the impact of the Blue-Green-based interventions on hundreds of participants across these three cities. The Feel Emotion Sensor is a wearable mental health device that uses proprietary algorithms to continuously monitor a person’s emotional state.

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#connected device

#mobile app

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Aug 07,2023

Dana-Farber AI traces unknown cancers back to their source: study

Researchers at Dana-Farber Cancer Institute have developed an artificial intelligence tool that could help trace a cancer back to its site of origin in particularly tricky cases. While a standard diagnostic work-up can include digital imaging scans, tumor biopsies and pathology reports, clinicians are still unable to find the original source of the malignancy in about 3% to 5% of patients. Known as cancers of unknown primary origin, they are cases of metastatic disease that has spread from a separate and hidden location in the body. The AI program, dubbed OncoNPC, uses sequencing data taken from tumor DNA to help predict where it came from. The computer model was built using medical records and genetic testing results gathered from more than 36,400 patients. OncoNPC was able to accurately predict the origin of about 80% of tumors, including metastatic disease, by linking them with known cancer types, according to Dana-Farber.

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#ai/software

#ml

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Aug 01,2023

Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

Pheochromocytoma and paraganglioma are rare tumors, and Pheochromocytoma and have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. In this machine learning modelling study, they used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. Results suggested that although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate the study's nine recommended features.

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#ml

#ai/software

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Aug 02,2023

AI shows its value in mammography screening

A study has shown that artificial intelligence can be safely used to support mammography to spot breast cancers and may spot more tumours than the current standard approach. The Swedish trial published in The Lancet Oncology found that one expert reader using ScreenPoint's Transpara AI to look at mammograms was at least as good, and possibly better, than the current standard of two expert readers. It is one of the most comprehensive assessments of AI in this setting to date, involving more than 80,000 women attending screening clinics in Sweden who were randomised to either the AI or the control group. Detection rates were six per 1,000 women screened with the AI, compared to five per 1,000 in the control group, with 244 and 203 cases found, respectively. The false-positive rate was 1.5% in both the AI group and the group assessed by two experts. The positive result means that the study will continue to accrue around 100,000 patients, with all subjects followed for at least two years to see if there are any differences between the two groups in the rate of interval cancer – breast cancer found during the three years after a normal result and before the next screening appointment – per 1,000 screens.

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#ai/software

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Aug 09,2023

Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study

There is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. Researchers propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules. They conducted a prospective specimen collection and retrospective masked evaluation study, and recruited participants (1097 enrolled from July 7, 2017, to Feb 12, 2019) with a solitary pulmonary nodule sized 5–30 mm from 24 hospitals across 20 cities in China. They developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). They then integrated the CIBM model with previously established circulating tumour DNA methylation model (PulmoSeek developed by AnchorDx) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models.

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#ai/software

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Aug 01,2023

Study Published in JNCI Cancer Spectrum Demonstrates Real-World Utility of Veracyte’s Decipher Prostate Genomic Classifier in Prostate Cancer Treatment

Veracyte announced the publication of a large, real-world study reinforcing the Decipher Prostate Genomic Classifier’s ability to guide personalized treatment approaches for men with prostate cancer. The study analyzed data from 8,297 patients in the SEER registry who received a primary prostate cancer diagnosis from 2010 to 2018 and underwent Decipher Prostate testing. Researchers evaluated the association between the patients’ Decipher scores (range 0-1) and risk groups (low, intermediate and high), and the use of active surveillance and watchful waiting (AS/WW) as well as adverse pathology at the time of radical prostatectomy (RP). The results demonstrated that AS/WW was highest among subjects with low-risk Decipher biopsy results (41%), compared to those who had intermediate-risk (27%) or high-risk (11%) scores. Among subjects with clinically low-risk prostate cancer, 65% of those with low-risk Decipher results were managed with AS/WW. "This study is further evidence that the Decipher Prostate test provides valuable clinical information to physicians and their patients with prostate cancer. In contemporary practice, physicians treat patients with higher-risk Decipher scores more aggressively, and patients with lower-risk Decipher scores more conservatively,” said Elai Davicioni, Ph.D., Veracyte’s medical director for Urology.

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#ai/software

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