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Public Health Public Health

Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis.

In International journal of medical informatics ; h5-index 49.0

OBJECTIVE : We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM) prediction in community settings and determine their predictive performance.

METHOD : Systematic review of ML predictive modelling studies in 13 databases since 2009 was conducted. Primary outcomes included metrics of discrimination, calibration, and classification. Secondary outcomes included important variables, level of validation, and intended use of models. Meta-analysis of c-indices, subgroup analyses, meta-regression, publication bias assessments and sensitivity analyses were conducted.

RESULTS : Twenty-three studies (40 prediction models) were included. Studies with high-, moderate-, and low- risk of bias were 3, 14, and 6 respectively. All studies conducted internal validation whereas none conducted external validation of their models. Twenty studies provided classification metrics to varying extents whereas only 7 studies performed model calibration. Eighteen studies reported information on both the variables used for model development and the feature importance. Twelve studies highlighted potential applicability of their models for T2DM screening. Meta-analysis produced a good pooled c-index (0.812). Sources of heterogeneity were identified through subgroup analyses and meta-regression. Issues pertaining to methodological quality and reporting were observed.

CONCLUSIONS : We found evidence of good performance of ML models for T2DM prediction in the community. Improvements to methodology, reporting and validation are needed before they can be used at scale.

Silva Kushan De, Lee Wai Kit, Forbes Andrew, Demmer Ryan T, Barton Christopher, Enticott Joanne


Diabetes mellitus, Diagnosis, Machine learning, Meta-Analysis, Prognosis, Type 2

Radiology Radiology

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PURPOSE : Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the limited number of imaging studies available at a single facility. This study aimed to find a simple and low-cost method to increase the accuracy of deep learning semantic segmentation for radiation therapy of prostate cancer.

METHODS : In total, 556 cases with non-contrast CT images for prostate cancer radiation therapy were examined using a two-dimensional U-Net. Initially, all slices were used for the input data. Then, we removed slices of the cranial portions, which were beyond the margins of the bladder and rectum. Finally, the ground truth labels for the bladder and rectum were added as channels to the input for the prostate training dataset.

RESULTS : The highest mean dice similarity coefficients (DSCs) for each organ in the test dataset of 56 cases were 0.85 ± 0.05, 0.94 ± 0.04 and 0.85 ± 0.07 for the prostate, bladder and rectum, respectively. Removal of the cranial slices from the original images significantly increased the DSC of the rectum from 0.83 ± 0.09 to 0.85 ± 0.07 (p < 0.05). Adding bladder and rectum information to prostate training without removing the slices significantly increased the DSC of the prostate from 0.79 ± 0.05 to 0.85 ± 0.05 (p < 0.05).

CONCLUSIONS : These cost-free approaches may be useful for new applications, which may include updated models and datasets. They may be applicable to other organs at risk (OARs) and clinical targets such as elective nodal irradiation.

Nemoto Takafumi, Futakami Natsumi, Yagi Masamichi, Kunieda Etsuo, Akiba Takeshi, Takeda Atsuya, Shigematsu Naoyuki


Deep learning, Prostate cancer, Semantic segmentation, U-Net

Ophthalmology Ophthalmology

Comparing Perimetric Loss at Different Target Intraocular Pressures for Patients with High Tension and Normal Tension Glaucoma.

In Ophthalmology. Glaucoma

OBJECTIVE : To compare forecasted changes in mean deviation (MD) on perimetry for patients with normal-tension glaucoma (NTG) and high-tension glaucoma (HTG) at different target intraocular pressures (IOPs) using a machine-learning technique called Kalman Filtering (KF).

DESIGN : Retrospective cohort study.

PARTICIPANTS : 496 patients with HTG from the Collaborative Initial Glaucoma Treatment Study or the Advanced Glaucoma Intervention Study and 262 patients with NTG from Japan.

METHODS : Using the first 5 sets of tonometry and perimetry measurements, we classified each patient as a fast-progressor, slow-progressor, or non-progressor. Using KF, we generated personalized forecasts of MD changes on perimetry over 2.5 years of follow-up for fast-progressors and slow-progressors with HTG and NTG whose IOPs were maintained at hypothetical IOP targets of 9-21 mmHg. We also assessed future MD loss with different percentage reductions in IOP from baseline (0-50%) for the groups.

MAIN OUTCOME MEASURES : Mean change in forecasted MD at different target IOPs.

RESULTS : The mean ± SD age of patients with NTG and HTG were 63.5±10.5 years and 66.5±10.9 years, respectively. At target IOPs of 9, 15, and 21, fast progressors with NTG had mean forecasted MD losses of 2.3±0.2, 4.0±0.2, and 5.7±0.2 dB and slow progressors had mean forecasted MD losses of 0.63±0.02, 1.02±0.03, and 1.49±0.07 dB over 2.5 years of follow up, respectively. At target IOPs of 9, 15, and 21, fast progressors with HTG had mean forecasted MD losses of 1.8±0.1, 3.4±0.1, and 5.1±0.1 dB and slow progressors had mean forecasted MD losses of 0.55±0.06, 1.04±0.08, and 1.59±0.10 dB over 2.5 years of follow up. Fast progressors with NTG experienced a greater MD decline than fast progressors with HTG at each target IOP (p≤0.007 for all). The MD decline for slow progressors with HTG and NTG were similar at each target IOP (p≥0.24 for all). Fast progressors with HTG experienced greater MD loss than those with NTG with IOP reductions of 0-10% from baseline (p≤0.01 for all) but not 25% (p=0.07) or 50% (p=0.76).

CONCLUSIONS : Machine learning algorithms using KF techniques demonstrate promise at forecasting future values of MD at different target IOPs for patients with NTG and HTG.

DeRoos Luke, Nitta Koji, Lavieri Mariel S, Van Oyen Mark P, Kazemian Pooyan, Andrews Chris A, Sugiyama Kazuhisa, Stein Joshua D


General General

3D printing tablets: predicting printability and drug dissolution from rheological data.

In International journal of pharmaceutics ; h5-index 67.0

Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material's performance and provide valuable insight regarding the material's macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 C. In contrast PCL was unextrudable at 130 and 150 C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100-1000 Pa.s at the apparent shear rate of the nozzle. The drug profile of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f2 similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile.

Elbadawi Moe, Gustaffson Thomas, Gaisford Simon, Basit Abdul W


3D Printed drug products, Artificial Intelligence, Fused Deposition Modeling (FDM), Machine Learning, Oral drug delivery systems, Prediction Models, Three-dimensional printing

Public Health Public Health

Detection of Glaucoma Deterioration in The Macular Region with Optical Coherence Tomography: Challenges and Solutions.

In American journal of ophthalmology ; h5-index 67.0

PURPOSE : Macular imaging with optical coherence tomography (OCT) measures the most critical retinal ganglion cells (RGC) in the human eye. The goal of this perspective is to review the challenges to detection of glaucoma progression with macular OCT imaging and propose ways to enhance its performance.

DESIGN : Perspective with review of relevant literature.

METHODS : Review of challenges and issues related to detection of change on macular OCT images in glaucoma eyes.




MAIN OUTCOME MEASURE : Confounding factors affecting detection of change on macular OCT images.

RESULTS : The main challenges to detection of structural progression in the macula include the magnitude of and the variable amount of test-retest variability among patients, the confounding effect of aging, lack of a reliable and easy-to-measure functional outcome or external standard, confounding effect of macular conditions including myopia, and the measurement floor of macular structural outcomes. Potential solutions to these challenges include controlling head tilt or torsion during imaging, estimating within-eye variability for individual patients, improved data visualization, use of artificial intelligence methods, and implementation of statistical approaches suitable for multidimensional longitudinal data.

CONCLUSIONS : Macular OCT imaging is a crucial structural imaging modality for assessing the central RGCs. Addressing the current shortcomings in acquisition and analysis of macular volume scans can enhance its utility for measuring the health of central RGCs and hence assist clinicians with timely institution of appropriate treatment.

Nouri-Mahdavi Kouros, Weiss Robert E


GCC, GCIPL, GCL, Macula, OCT, Optical Coherence Tomography, glaucoma, progression, retinal ganglion cells

Cardiology Cardiology

Primary Prevention Trial Designs Using Coronary Imaging: A National Heart, Lung, and Blood Institute Workshop.

In JACC. Cardiovascular imaging

Coronary artery calcium (CAC) is considered a useful test for enhancing risk assessment in the primary prevention setting. Clinical trials are under consideration. The National Heart, Lung, and Blood Institute convened a multidisciplinary working group on August 26 to 27, 2019, in Bethesda, Maryland, to review available evidence and consider the appropriateness of conducting further research on coronary artery calcium (CAC) testing, or other coronary imaging studies, as a way of informing decisions for primary preventive treatments for cardiovascular disease. The working group concluded that additional evidence to support current guideline recommendations for use of CAC in middle-age adults is very likely to come from currently ongoing trials in that age group, and a new trial is not likely to be timely or cost effective. The current trials will not, however, address the role of CAC testing in younger adults or older adults, who are also not addressed in existing guidelines, nor will existing trials address the potential benefit of an opportunistic screening strategy made feasible by the application of artificial intelligence. Innovative trial designs for testing the value of CAC across the lifespan were strongly considered and represent important opportunities for additional research, particularly those that leverage existing trials or other real-world data streams including clinical computed tomography scans. Sex and racial/ethnic disparities in cardiovascular disease morbidity and mortality, and inclusion of diverse participants in future CAC trials, particularly those based in the United States, would enhance the potential impact of these studies.

Greenland Philip, Michos Erin D, Redmond Nicole, Fine Lawrence J, Alexander Karen P, Ambrosius Walter T, Bibbins-Domingo Kirsten, Blaha Michael J, Blankstein Ron, Fortmann Stephen P, Khera Amit, Lloyd-Jones Donald M, Maron David J, Min James K, Muhlestein J Brent, Nasir Khurram, Sterling Madeline R, Thanassoulis George


calcium, coronary, prevention, trials