Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

Larger thalamus correlated with inattentive severity in the inattentive subtype of ADHD without comorbidity.

Previous studies of brain structural abnormalities in attention-deficit/hyperactivity disorder (ADHD) samples scarcely excluded comorbidity or analyzed them in subtypes. This study aimed to identify neuroanatomical alterations related to diagnosis and subtype of ADHD participants without comorbidity. In our cross-sectional analysis, we used T1-weighted structural MRI images of individuals from the ADHD-200 database. After strict exclusion, 121 age-matched children with uncomorbid ADHD (54 with ADHD-inattentive [iADHD] and 67 with ADHD-combined [cADHD]) and 265 typically developing control subjects (TDC) were included in current investigation. The established method of voxel-based morphometry (VBM8) was used to assess global brain volume and regional grey matter volume (GM). Our results showed that the ADHD patients had more regional GM in the bilateral thalamus relative to the controls. Post hoc analysis revealed that regional GM increase only linked to the iADHD subtype in the right thalamus and precentral gyrus. Besides, the right thalamus volume was positively related to inattentive severity in the iADHD. There were no group differences in global volume. Our results provide preliminary evidence that cerebral structural alterations are tied to uncomorbid ADHD subjects and predominantly attribute to iADHD subtype. Furthermore, the volume of the right thalamus may be relevant to inattentive symptoms in iADHD possibly related to a lack of inhibition of irrelevant sensory input.