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In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.
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In this paper, we demonstrate a novel hybrid 3C-silicon carbide-lithium niobate (3C-SiC-LN) platform for passive and active integrated nanophotonic devices enabled through wafer bonding. These devices are fabricated by etching the SiC layer, with the hybrid optical mode power distributed between SiC and LN layers through a taper design. We present a racetrack resonator-based electro-optic (EO) phase shifter where the resonator is fabricated in SiC while using LN for EO-effect (r33≈ 27 pm/V). The proposed phase shifter demonstrates efficient resonance wavelength tuning with low voltage-length product (Vπ.Lπ ≈ 2.18â V cm) using the EO effect of LN. This hybrid SiC-LN platform would enable high-speed, low-power, and miniaturized photonic devices (e.g., modulators, switches, filters) operable over a broad range of wavelengths (visible to infrared) with applications in both classical and quantum nanophotonics.
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An ultra-narrow 40-nm slotted waveguide is fabricated to enable highly efficient, electro-optic polymer modulators. Our measurement results indicate that VπL's below â¼ 1.19 V.mm are possible for the balanced Mach-Zehnder modulators using this ultra-narrow slotted waveguide on a hybrid silicon-organic hybrid platform. Our simulations suggest that VπL's can be further reduced to â¼ 0.35 V.mm if appropriate doping is utilized. In addition to adapting standard recipes, we developed two novel fabrication processes to achieve miniaturized devices with high modulation sensitivity. To boost compactness and decrease the overall footprint, we use a fabrication approach based on air bridge interconnects on thick, thermally-reflowed, MaN 2410 E-beam resist protected by an alumina layer. To overcome the challenges of high currents and imperfect infiltration of polymers into ultra-narrow slots, we use a carefully designed, atomically-thin layer of TiO2 as a carrier barrier to enhance the efficiency of our electro-optic polymers. The anticipated increase in total capacitance due to the TiO2 layer is negligible. Applying our TiO2 surface treatment to the ultra-narrow slot allows us to obtain an improved index change efficiency (∂n/∂V) of â¼ 22% for a 5 nm TiO2 layer. Furthermore, compared to non-optimized cases, our peak measured current during poling is reduced by a factor of â¼ 3.
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OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.
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COVID-19 , Algoritmos , Inteligencia Artificial , COVID-19/diagnóstico por imagen , Humanos , Masculino , Persona de Mediana Edad , Radiografía Torácica , Radiólogos , Estudios RetrospectivosRESUMEN
Phase-change materials (PCMs) offer a compelling platform for active metaoptics, owing to their large index contrast and fast yet stable phase transition attributes. Despite recent advances in phase-change metasurfaces, a fully integrable solution that combines pronounced tuning measures, i.e., efficiency, dynamic range, speed, and power consumption, is still elusive. Here, we demonstrate an in situ electrically driven tunable metasurface by harnessing the full potential of a PCM alloy, Ge2Sb2Te5 (GST), to realize non-volatile, reversible, multilevel, fast, and remarkable optical modulation in the near-infrared spectral range. Such a reprogrammable platform presents a record eleven-fold change in the reflectance (absolute reflectance contrast reaching 80%), unprecedented quasi-continuous spectral tuning over 250 nm, and switching speed that can potentially reach a few kHz. Our scalable heterostructure architecture capitalizes on the integration of a robust resistive microheater decoupled from an optically smart metasurface enabling good modal overlap with an ultrathin layer of the largest index contrast PCM to sustain high scattering efficiency even after several reversible phase transitions. We further experimentally demonstrate an electrically reconfigurable phase-change gradient metasurface capable of steering an incident light beam into different diffraction orders. This work represents a critical advance towards the development of fully integrable dynamic metasurfaces and their potential for beamforming applications.
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A new, to the best of our knowledge, device platform for tuning the resonance wavelength of integrated photonic resonators based on polysilicon-based micro-heaters for complementary metal-oxide semiconductor (CMOS)-foundry-based active Si photonics is demonstrated. The miniaturized micro-heater can be placed directly on the active Si layer, with a pedestal providing the optical and electrical isolation needed for the implementation of ultrafast active photonic devices such as modulators. The demonstrated devices do not require any additional modifications to the standard CMOS foundry processes. Experiments demonstrate a tuning efficiency of 0.25â nm/mW (or 42â GHz/mW) for a 5-µm-radius microdisk resonator with a loaded quality factor (Q) > 35,000. This polysilicon-based heater demonstrates a tunability of 42â GHz/mW with an average switching time of 60 µs. The proposed compact heater architecture enables it to be kept near to the optical mode, thereby providing efficient and high-speed wavelength tuning for resonant devices.
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Subwavelength nonlinear optical sources with high efficiency have received extensive attention, although strong dynamic controllability of these sources is still elusive. Germanium antimony telluride (GST) as a well-established phase-change chalcogenide is a promising candidate for the reconfiguration of subwavelength nanostructures due to the strong non-volatile change of the index of refraction between its amorphous and crystalline states. Here, we numerically demonstrate an electromagnetically-induced-transparency-based silicon metasurface actively controlled with a quarter-wave asymmetric Fabry-Perot cavity incorporating GST to modulate the relative phase of incident and reflected pump beams. We demonstrate a giant third-harmonic generation (THG) switch with a modulation depth as high as â¼70dB at the resonant band. We also demonstrate the possibility of multi-level THG amplitude modulation for the fundamental C-band by controlling the crystallization fraction of GST at multiple intermediate states. This study shows the high potential of GST-based fast dynamic nonlinear photonic switches for real-world applications ranging from communications to optical computing.
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We demonstrate, to the best of our knowledge, the first high-Q silicon carbide (SiC) integrated photonic sensor based on slot-ring resonators on a 3C-SiC-on-insulator (SiCOI) platform. We experimentally demonstrate an intrinsic Q of 17,400 at around 1310 nm wavelength for a slot-ring resonator with 40 µm radius with water cladding. By applying different concentrations of a sodium chloride (NaCl) solution that covers the devices, measured bulk sensitivities of 264-300 nm/RIU (refractive index unit) are achieved in the slot-ring resonator with a 400-450 nm rail width and a 100-200 nm slot width. The device performance for biomolecular layer sensing (BMLS) is proved by the detection of the cardiac biomarker troponin with 248-322 pm/nm surface sensitivity. The reported slot-ring resonators can be of great interest for diverse sensing applications from visible to infrared wavelengths.
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Inducing a large refractive-index change is the holy grail of reconfigurable photonic structures, a goal that has long been the driving force behind the discovery of new optical material platforms. Recently, the unprecedentedly large refractive-index contrast between the amorphous and crystalline states of Ge-Sb-Te (GST)-based phase-change materials (PCMs) has attracted tremendous attention for reconfigurable integrated nanophotonics. Here, we introduce a microheater platform that employs optically transparent and electrically conductive indium-tin-oxide (ITO) bridges for the fast and reversible electrical switching of the GST phase between crystalline and amorphous states. By the proper assignment of electrical pulses applied to the ITO microheater, we show that our platform allows for the registration of virtually any intermediate crystalline state into the GST film integrated on the top of the designed microheaters. More importantly, we demonstrate the full reversibility of the GST phase between amorphous and crystalline states. To show the feasibility of using this hybrid GST/ITO platform for miniaturized integrated nanophotonic structures, we integrate our designed microheaters into the arms of a Mach-Zehnder interferometer to realize electrically reconfigurable optical phase shifters with orders of magnitude smaller footprints compared to existing integrated photonic architectures. We show that the phase of optical signals can be gradually shifted in multiple intermediate states using a structure that can potentially be smaller than a single wavelength. We believe that our study showcases the possibility of forming a whole new class of miniaturized reconfigurable integrated nanophotonics using beyond-binary reconfiguration of optical functionalities in hybrid PCM-photonic devices.
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In this Letter, we present a deep-learning-based method using neural networks (NNs) for inverse design of photonic nanostructures. We show that by using dimensionality reduction in both the design and the response spaces, the computational complexity of the inverse design algorithm is considerably reduced. As a proof of concept, we apply this method to design multi-layer thin-film structures composed of consecutive layers of two different dielectrics and compare the results using our techniques to those using conventional NNs.
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We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.
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COVID-19/diagnóstico , Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Área Bajo la Curva , Inteligencia Artificial , COVID-19/diagnóstico por imagen , Simulación por Computador , Progresión de la Enfermedad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Redes Neurales de la Computación , Radiografía , Índice de Severidad de la EnfermedadRESUMEN
We report, to the best of our knowledge, the first demonstration of integrated electro-optic (EO) phase shifters based on racetrack microresonators on a 3C silicon-carbide-on-insulator (SiCOI) platform working at near-infrared wavelengths. By applying DC voltage in the crystalline axis perpendicular to the waveguide plane, we have observed optical phase shifts from the racetrack microresonators whose loaded quality ($ Q $) factors are $\sim\! {30,\!000}$. We show voltage-length product (${{V}_{\pi}} \cdot {{L}_{ \pi}}$) of ${118}\;{{\rm V}\cdot{\rm cm}}$, which corresponds to an EO coefficient ${{r}_{41}}$ of 2.6 pm/V. The SiCOI platform can be used to realize tunable silicon carbide integrated photonic devices that are desirable for applications in nonlinear and quantum photonics over a wide bandwidth that covers visible and infrared wavelengths.
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Efficient hybrid plasmonic-photonic metasurfaces that simultaneously take advantage of the potential of both pure metallic and all-dielectric nanoantennas are identified as an emerging technology in flat optics. Nevertheless, postfabrication tunable hybrid metasurfaces are still elusive. Here, we present a reconfigurable hybrid metasurface platform by incorporating the phase-change material Ge2Sb2Te5 (GST) into metal-dielectric meta-atoms for active and nonvolatile tuning of properties of light. We systematically design a reduced-dimension meta-atom, which selectively controls the hybrid plasmonic-photonic resonances of the metasurface via the dynamic change of optical constants of GST without compromising the scattering efficiency. As a proof-of-concept, we experimentally demonstrate two tunable metasurfaces that control the amplitude (with relative modulation depth as high as ≈80%) or phase (with tunability >230°) of incident light promising for high-contrast optical switching and efficient anomalous to specular beam deflection, respectively. Our findings further substantiate dynamic hybrid metasurfaces as compelling candidates for next-generation reprogrammable meta-optics.
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An integrated photonic platform is proposed for strong interactions between atomic beams and annealing-free high-quality-factor (Q) microresonators. We fabricated a thin-film, air-clad SiN microresonator with a loaded Q of 1.55×106 around the optical transition of 87Rb at 780 nm. This Q is achieved without annealing the devices at high temperatures, enabling future fully integrated platforms containing optoelectronic circuitry. The estimated single-photon Rabi frequency (2g) is 2π×64MHz 100 nm above the resonator. Our simulation result indicates that miniature atomic beams with a longitudinal speed of 0.2 m/s to 30 m/s will interact strongly with our resonator, allowing for the detection of single-atom transits and realization of scalable single-atom photonic devices. Interactions between racetrack resonators and thermal atomic beams are also simulated.
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Heterostructures of two-dimensional transition metal dichalcogenides (TMDs) can offer a plethora of opportunities in condensed matter physics, materials science, and device engineering. However, despite state-of-the-art demonstrations, most current methods lack enough degrees of freedom for the synthesis of heterostructures with engineerable properties. Here, we demonstrate that combining a postgrowth chalcogen-swapping procedure with the standard lithography enables the realization of lateral TMD heterostructures with controllable dimensions and spatial profiles in predefined locations on a substrate. Indeed, our protocol receives a monolithic TMD monolayer (e.g., MoSe2) as the input and delivers lateral heterostructures (e.g., MoSe2-MoS2) with fully engineerable morphologies. In addition, through establishing MoS2xSe2(1-x)-MoS2ySe2(1-y) lateral junctions, our synthesis protocol offers an extra degree of freedom for engineering the band gap energies up to â¼320 meV on each side of the heterostructure junction via changing x and y independently. Our electron microscopy analysis reveals that such continuous tuning stems from the random intermixing of sulfur and selenium atoms following the chalcogen swapping. We believe that, by adding an engineering flavor to the synthesis of TMD heterostructures, our study lowers the barrier for the integration of two-dimensional materials into practical optoelectronic platforms.
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Atomically thin transition metal dichalcogenides (TMDs) in their excited states can serve as exceptionally small building blocks for active optical platforms. In this scheme, optical excitation provides a practical approach to control light-TMD interactions via the photocarrier generation, in an ultrafast manner. Here, it is demonstrated that via a controlled generation of photocarriers the second-harmonic generation (SHG) from a monolayer MoS2 crystal can be substantially modulated up to ≈55% within a timeframe of ≈250 fs, a set of performance characteristics that showcases the promise of low-dimensional materials for all-optical nonlinear data processing. The combined experimental and theoretical study suggests that the large SHG modulation stems from the correlation between the second-order dielectric susceptibility χ(2) and the density of photoexcited carriers in MoS2 . Indeed, the depopulation of the conduction band electrons, at the vicinity of the high-symmetry K/K' points of MoS2 , suppresses the contribution of interband electronic transitions in the effective χ(2) of the monolayer crystal, enabling the all-optical modulation of the SHG signal. The strong dependence of the second-order optical response on the density of photocarriers reveals the promise of time-resolved nonlinear characterization as an alternative route to monitoring carrier dynamics in excited states of TMDs.
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Coupled-mode theory (CMT) has been widely used in optics and photonics design. Despite its popularity, several different formulations of CMT exist in the literature, and their applicable range is not entirely clear, in particular when it comes to high-index-contrast photonics platforms. Here we propose an improved formulation of CMT and demonstrate its superior performance through numerical simulations that compare CMT-derived quantities with supermode calculations and full wave propagation simulations. In particular, application of the improved CMT to asymmetric waveguides reveals a necessary correction in the conventional phase matching condition for high-index-contrast systems, which could lead to more accurate photonic circuit designs involving asymmetric elements.
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In contrast to lossy plasmonic metasurfaces (MSs), wideband dielectric MSs comprising subwavelength nanostructures supporting Mie resonances are of great interest in the visible wavelength range. Here, for the first time to our knowledge, we experimentally demonstrate a reflective MS consisting of a square-lattice array of hafnia (HfO2) nanopillars to generate a wide color gamut. To design and optimize these MSs, we use a deep-learning algorithm based on a dimensionality reduction technique. Good agreement is observed between simulation and experimental results in yielding vivid and high-quality colors. We envision that these structures not only empower the high-resolution digital displays and sensitive colorimetric biosensors but also can be applied to on-demand applications of beaming in a wide wavelength range down to deep ultraviolet.
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We demonstrate, to the best of our knowledge, the first thermally reconfigurable high-Q silicon carbide (SiC) microring resonators with integrated microheaters on a 3C-SiC-on-insulator platform. We extract a thermo-optic coefficient of around 2.67×10-5/K for 3C-SiC from wavelength shift of a resonator heated by a hot plate. Finally, we fabricate a 40-µm-radius microring resonator with intrinsic Q of 139,000 at infrared wavelengths (â¼1550 nm) after integration with a NiCr microheater. By applying current through the microheater, a resonance shift of 30 pm/mW is achieved in the microring, corresponding to â¼50 mW per π phase shift. This platform offers an easy and reliable way for integration with electronic devices as well as great potential for diverse integrated optics applications.
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We report a high-quality 3C-silicon carbide (SiC)-on-insulator (SiCOI) integrated photonic material platform formed by wafer bonding of crystalline 3C-SiC to a silicon oxide (SiO2)-on-silicon (Si) substrate. This material platform enables to develop integrated photonic devices in SiC without the need for undercutting the Si substrate, in contrast to the structures formed on conventional 3C-SiC-on-Si platforms. In addition, we show a unique process in the SiCOI platform for minimizing the effect of lattice mismatch during the growth of SiC on Si through polishing after bonding. This results in a high-quality SiCOI platform that enables record high Qs of 142,000 in 40 µm radius SiC microring resonators. The resulting SiCOI platform has a great potential for a wide range of applications in integrated optics, including nonlinear optical devices, quantum optical devices, and high-power optical devices.