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Objective To implement and evaluate a large-scale online cervical cancer screening programme in Hubei Province, China, supported by artificial intelligence and delivered by trained health workers. Methods The screening programme, which started in 2017, used four types of health worker: sampling health workers, slide preparation technicians, diagnostic health workers and cytopathologists. Sampling health workers took samples from the women on site; slide preparation technicians prepared slides for liquid-based cytology; diagnostic health workers identified negative samples and classified positive samples based on the Bethesda System after cytological assessment using online artificial intelligence; and cytopathologists reviewed positive samples and signed reports of the results online. The programme used fully automated scanners, online artificial intelligence, an online screening management platform, and mobile telephone devices to provide screening services. We evaluated the sustainability, performance and cost of the programme. Results From 2017 to 2021, 1 518 972 women in 16 cities in Hubei Province participated in the programme, of whom 1 474 788 (97.09%) had valid samples for the screening. Of the 86 648 women whose samples were positive, 30 486 required a biopsy but only 19 495 had one. The biopsy showed that 2785 women had precancerous lesions and 191 had invasive cancers. The cost of screening was 6.31 United States dollars (US$) per woman for the public payer: US$ 1.03 administrative costs and US$ 5.28 online screening costs. Conclusion Cervical cancer screening using artificial intelligence in Hubei Province provided a low-cost, accessible and effective service, which will contribute to achieving universal cervical cancer screening coverage in China. Objectif Deployer et evaluer un programme en ligne de grande envergure destine au depistage du cancer du col de l'uterus dans la province du Hubei, en Chine, reposant sur l'intelligence artificielle et mis en oeuvre par des agents de sante formes a cet effet. Methodes Le programme de depistage, qui a debute en 2017, s'appuyait sur quatre types d'agents de sante: des agents charges de prelever les echantillons, des techniciens charges de preparer les lames, des agents charges d'etablir le diagnostic et des cytopathologistes. Les premiers ont preleve des echantillons aupres des femmes sur place, les deuxiemes ont prepare les lames pour la cytologie en phase liquide, les troisiemes ont identifie les echantillons negatifs et classe les echantillons positifs selon le systeme de Bethesda apres analyse cytologique a l'aide d'une intelligence artificielle en ligne, tandis que les cytopathologistes ont examine les echantillons positifs et signe les rapports de resultats en ligne. Le programme etait constitue de scanners entierement automatises, d'une intelligence artificielle en ligne, d'une plateforme de gestion du depistage en ligne, ainsi que d'appareils de telephonie mobile pour les services de depistage. Nous avons evalue la viabilite, les performances et le cout d'un tel programme. Resultats Entre 2017 et 2021, 1 518 972 femmes residant dans 16 villes de la province du Hubei ont participe au programme, et 1 474 788 (97,09%) d'entre elles ont presente des echantillons valides pour le depistage. Sur les 86 648 femmes dont les echantillons se sont reveles positifs, 30 486 ont eu besoin d'une biopsie, mais seulement 19 495 en ont effectue une. La biopsie a montre que 2785 femmes presentaient des lesions precancereuses et que 191 etaient atteintes de cancers invasifs. Le cout du depistage s'elevait a 6,31 dollars americains (USD) par femme pour le payeur public: 1,03 USD pour les frais administratifs et 5,28 USD pour le depistage en ligne. Conclusion Le depistage du cancer du col de l'uterus assiste par intelligence artificielle dans la province du Hubei est un service abordable, accessible et efficace, qui contribuera a garantir une couverture universelle en la matiere dans le pays. Objetivo Implementar y evaluar un programa de deteccion del cancer del cuello uterino en linea a gran escala en la provincia de Hubei (China), con el apoyo de la inteligencia artificial y a cargo de trabajadores sanitarios formados. Metodos El programa de deteccion, que comenzo en 2017, utilizo cuatro tipos de trabajadores sanitarios: trabajadores sanitarios de muestreo, tecnicos de preparacion de portaobjetos, trabajadores sanitarios de diagnostico y citopatologos. Los trabajadores sanitarios de muestreo tomaban muestras de las mujeres in situ, los tecnicos de preparacion de portaobjetos preparaban los portaobjetos para la citologia mediante liquidos, los trabajadores sanitarios de diagnostico identificaban las muestras negativas y clasificaban las muestras positivas segun el Sistema Bethesda tras la evaluacion citologica mediante inteligencia artificial en linea, y los citopatologos revisaban las muestras positivas y firmaban los informes de los resultados en linea. El programa utilizo escaneres totalmente automatizados, inteligencia artificial en linea, una plataforma de gestion de la deteccion en linea y dispositivos de telefonia movil para proporcionar servicios de deteccion. Se evaluaron la sostenibilidad, el rendimiento y el coste del programa. Resultados Entre 2017 y 2021, 1 518 972 mujeres de 16 ciudades de la provincia de Hubei participaron en el programa, de las que 1 474 788 (97,09%) tenian muestras validas para la deteccion. De las 86 648 mujeres cuyas muestras dieron positivo, 30 486 necesitaron una biopsia, pero solo 19 495 se sometieron a ella. La biopsia mostro que 2785 mujeres tenian lesiones precancerosas y 191 canceres invasivos. El coste de la deteccion fue de 6,31 dolares estadounidenses (US$) por mujer para el organismo publico pagador: US$ 1,03 de costes administrativos y US$ 5,28 de costes de deteccion en linea. Conclusion La deteccion del cancer del cuello uterino mediante inteligencia artificial en la provincia de Hubei proporciono un servicio de bajo coste, accesible y eficaz, que contribuira a lograr la cobertura universal de deteccion del cancer del cuello uterino en China., Introduction Cervical cancer is a threat to women's health. According to data released by the International Agency for Research on Cancer, the cancer research agency of the World Health Organization [...]

In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers’ steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.