###### Abstract
Additionally, size and thickness of filter papers on electrodes of the first gel electrophoresis should be large and thick enough to absorb all salts gathering to the electrodes. Furthermore, the proper amounts of total proteins on the gels are different and should be optimized for each case. As for human serum, the amount was optimized by counting spots on the gels. Finally, although total number of the spots has not been raised compared with the conventional methods, other performances including high throughput capacity, high sensitivity, and high reproducibility have been raised enough for the practical applications. For clinical applications, large amounts of data should be obtained in a short time period from small amounts of specimens. The quality of the data must be high for intercomparisons. Conventional techniques could hardly be available for the purpose. On the other hand, our technique can solve these issues and can provide a sufficient amount of proteomics data to satisfy the necessary conditions for clinical use [3]. However, one issue still impedes realization of clinical proteomics using HP-2D-PAGE: how to facilitate high-throughput processing of vast numbers of 2D-PAGE images. Here, the first success of 2D-PAGE image processing using artificial intelligence (AI) is reported. If 2D-PAGE images can be processed automatically and quickly, large amounts of clinical data providing vital information can be utilized clinically. Furthermore, this breakthrough opens new avenues for data mining. Typically, 2D-PAGE spots that drastic changes in intensity as a patient's condition progresses are considered significant in terms of diagnosis. However, the proteins associated with such spots are not necessarily important for determining clinical state. In the cases of a number of diseases, multiple but slight changes in proteins are thought to induce disease onsets. It can be difficult to manually identify such significant spots on 2D-PAGE images. However, AI provides a principally different way of looking at these data and can be used to identify essential, significant spots from among large numbers of spots on 2D-PAGE images. Sepsis was chosen as the first target for AI-aided diagnosis in this study for various reasons. Sepsis is generally classified by severity, with severe sepsis characterized by a state of organ dysfunction, organ perfusion loss, or hypotension; septic shock, in particular, is characterized by persistent hypotension despite large-volume intravenous infusion [4]. Mortality is generally high in severe sepsis, with common case fatality rates of 20 to 30% [5; 6; 7]. Early stage treatment is strongly related to reduced mortality rate, but the pathogenic mechanisms of sepsis are very wide-ranging and complex. For this reason, sepsis treatment largely comprises symptomatic therapy. Moreover, diagnosis is often sought at a location other than the hospital in which treatment occurs. The need for AI-aided diagnosis is therefore particularly critical for prompt, appropriate treatment. AI-aided diagnosis holds the promise of faster, more accurate diagnosis of this complex pathology. It also holds promise of providing new findings that could not be obtained via manual visual inspection of data. In this study, therefore, we evaluated a fast and accurate AI-aided method for the diagnosis of sepsis. Practical development of AI-aided diagnosis using 2D-GE images posed two key problems. First, 2D-GE images generally exhibit very low quality and poor reproducibility compared with data used in AI image diagnosis already in practical use, such as facial authentication and computed tomography (CT) images. Images obtained with previously developed methods exhibit clear contrast, good quality, and good reproducibility in comparison with those obtained with existing methods but are substantially inferior in comparison with those already practically developed. The question arises then as to whether the 2D-GE images obtained in this study could be used in machine learning for AI-aided diagnosis.