Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans

OBJECTIVE In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid haemorrhage (SAH) on head computed tomography (CT) scans, and that the trained model performs satisfactorily when tested using external and real-world data. METHODS We used non-contrast head CT images of patients admitted Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e. delineated) SAH on 90 head CT scans, and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external datasets (137 SAH and 1242 control cases) collected in two foreign countries, and also by creating a dataset of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. RESULTS In the external validation set of 1379 cases, the algorithm identified 136 out of 137 SAH cases correctly (sensitivity 99.3%, specificity 63.2%). Of the 49064 axial head CT slices, the algorithm identified and localized SAH in 1845 out of 2110 slices with SAH (sensitivity 87.4%, specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0%, specificity 75.3%). The slice-level (27167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, as the algorithm identified and localized SAH in 58 out of 77 slices with SAH. The performance of the algorithm can be tested on through a webservice. CONCLUSIONS We show that the shared algorithm identifies SAH cases with a high sensitivity, and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing and reporting deep learning algorithms developed for medical imaging diagnostics. CLASSIFICATION OF EVIDENCE This study provides Class III evidence a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan

Transition to Superfluid Turbulence

Turbulence in superfluids depends crucially on the dissipative damping in vortex motion. This is observed in the B phase of superfluid 3He where the dynamics of quantized vortices changes radically in character as a function of temperature. An abrupt transition to turbulence is the most peculiar consequence. As distinct from viscous hydrodynamics, this transition to turbulence is not governed by the velocity-dependent Reynolds number, but by a velocity-independent dimensionless parameter 1/q which depends only on the temperature-dependent mutual friction -- the dissipation which sets in when vortices move with respect to the normal excitations of the liquid. At large friction and small values of 1/q < 1 the dynamics is vortex number conserving, while at low friction and large 1/q > 1 vortices are easily destabilized and proliferate in number. A new measuring technique was employed to identify this hydrodynamic transition: the injection of a tight bundle of many small vortex loops in applied vortex-free flow at relatively high velocities. These vortices are ejected from a vortex sheet covering the AB interface when a two-phase sample of 3He-A and 3He-B is set in rotation and the interface becomes unstable at a critical rotation velocity, triggered by the superfluid Kelvin-Helmholtz instability.Comment: Short review; to be published in Journal of Low Temperature Physics (2006

The Josephson heat interferometer

The Josephson effect represents perhaps the prototype of macroscopic phase coherence and is at the basis of the most widespread interferometer, i.e., the superconducting quantum interference device (SQUID). Yet, in analogy to electric interference, Maki and Griffin predicted in 1965 that thermal current flowing through a temperature-biased Josephson tunnel junction is a stationary periodic function of the quantum phase difference between the superconductors. The interplay between quasiparticles and Cooper pairs condensate is at the origin of such phase-dependent heat current, and is unique to Josephson junctions. In this scenario, a temperature-biased SQUID would allow heat currents to interfere thus implementing the thermal version of the electric Josephson interferometer. The dissipative character of heat flux makes this coherent phenomenon not less extraordinary than its electric (non-dissipative) counterpart. Albeit weird, this striking effect has never been demonstrated so far. Here we report the first experimental realization of a heat interferometer. We investigate heat exchange between two normal metal electrodes kept at different temperatures and tunnel-coupled to each other through a thermal `modulator' in the form of a DC-SQUID. Heat transport in the system is found to be phase dependent, in agreement with the original prediction. With our design the Josephson heat interferometer yields magnetic-flux-dependent temperature oscillations of amplitude up to ~21 mK, and provides a flux-to-temperature transfer coefficient exceeding ~ 60mK/Phi_0 at 235 mK [Phi_0 2* 10^(-15) Wb is the flux quantum]. Besides offering remarkable insight into thermal transport in Josephson junctions, our results represent a significant step toward phase-coherent mastering of heat in solid-state nanocircuits, and pave the way to the design of novel-concept coherent caloritronic devices.Comment: 4+ pages, 3 color figure