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Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psychological disorders. This exerts a negative impact on individuals, families and society. Timely diagnosis and intervention are crucial to prevent permanent damage to vision and stereopsis. Presently, strabismus diagnosis is reliant on the ophthalmologists′ evaluations which results in a lack of efficiency and coverage. However, routine school screening proves inadequate in assessing strabismus degree with low accuracy. Therefore, how to improve the efficiency of strabismus screening is an issue of great importance. This paper delves into the present landscape of strabismus diagnosis and treatment, considering both local and global research advancements. It focuses on the evolution of artificial intelligence technology, illuminating the utilization of artificial intelligence models and algorithms in strabismus. By pinpointing and exploring their strengths and limitations, it offers valuable insights, paving the way for future investigations into artificial intelligence-assisted strabismus diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke (AIS) is characterized by a high rate of disability and mortality, and rapid and accurate diagnosis and appropriate treatment are the keys. The severity of AIS patients depends on the location of early ischemic changes (EIC) in the brain tissue and the size of the lesion, so it is necessary to evaluate the patients' conditions by combining with imaging. The Alberta stroke program early CT score (ASPECT score) is a 10-point imaging score system that evaluates the EIC in the middle cerebral artery supplying region of the stroke patients in an accurate and simple way by quantifying the severity in terms of the imaging to help clinicians make medical decisions. ASPECT score is not only widely used to predict the prognosis of stroke patients, but also to identify the population benefiting from the endovascular therapy and evaluate the risk of endovascular therapy for patients. ASPECT score combined with multimodal imaging has also been used to predict ischemic core volumes and the prognosis. In recent years, with the development of artificial intelligence, automatic ASPECT scoring methods based on machine learning have emerged. This paper reviews ASPECT scoring methods, its value in the treatment and the assessment of prognosis in AIS, multimodal ASPECT scoring, and the application of automated ASPECT scoring combined with artificial intelligence. [ABSTRACT FROM AUTHOR]

Musculoskeletal diseases are diverse. Medical imaging examination is the primary means of disease diagnosis. Reasonable selection of image post-processing methods can provide reliable evaluation basis for disease diagnosis, surgery and prognosis. As a new stage of the development of computer science, artificial intelligence technology can efficiently and accurately preprocess and analyze images, and assist clinicians in diagnosis and treatment. It will bring new opportunities for the development of musculoskeletal imaging. This paper reviews the current situation and challenges of artificial intelligence in the field of imaging diagnosis of musculoskeletal system diseases, in order to provide some reference for the relevant research scholars. [ABSTRACT FROM AUTHOR]