A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects.

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

Longitudinal Mode Number Estimation of External Cavity Diode Laser Using Dual Periodic Grating for Optical Profiler System.

The concept of an optical profiler based on optical resonance was proposed, highlighting the initial requirements for mode number estimation. We proposed a method for estimating the longitudinal mode number of a laser propagating in an external cavity diode laser with high accuracy, utilizing dual-periodic diffraction gratings. These gratings were fabricated using interference lithography. To estimate the mode number, the wavelengths of two different modes are compared. Therefore, the greater the difference between the wavelengths, the higher the accuracy of the mode number determination. While the mode number difference was approximately 35 when using a conventional diffraction grating, this could be increased by a factor of 20 to around 700 using the dual-periodic grating. The relative accuracy achieved was 1.4 × 10-5.

Exploration and time-serial validation of logistic regression models composed of multiple laboratory tests for early detection of HCV-associated hepatocellular carcinoma.

BACKGROUND: We developed a laboratory test-based regression model for early detection of hepatocellular carcinoma (HCC) associated with HCV in its surveillance. METHODS: This matched case-control study was conducted by enrolling 452 patients with chronic hepatitis and/or cirrhosis, including 129 patients complicated with HCC. One-to-one propensity score matching was performed by referring to sex, age, and fibrosis-4 index, which resulted in 102 patients each in HCC and non-HCC groups. Logistic regression models (LRM) for distinguishing the two groups were explored from variable combinations of laboratory tests. The model was validated by our new scheme of applying it retroactively to trimonthly previous datasets. RESULTS: Models with a practical level of diagnostic accuracy (C-statistic) were α-fetoprotein (AFP) alone (0.810), LRM3 comprising AFP, AST, and ALT (0.850), and LRM4 comprising AFP, AFP/(AST × ALT), and AST (0.862). After retroactive application of each model, LRM4 showed the highest distinction of the two groups at -12M, -6M, -3M with C-statistics of 0.654, 0.786, 0.834, respectively. LRM4 was accurate even after limiting cases to early-stage HCC. CONCLUSIONS: LRM4 was proved useful in prompting clinicians to perform timely image study in the surveillance. The retroactive validation scheme is applicable to assess diagnostic models of other neoplastic diseases.