Vision, body and interpretation in medical imaging diagnostics

This article explores the profound impact of visualism and visual perception in the context of medical imaging diagnostics. It emphasizes the intricate interplay among vision, embodiment, subjectivity, language, and historicity within the realm of medical science and technology, with a specific focus on image consciousness. The study delves into the role of subjectivity in perception, facilitating the communication of opacity and historicity to the perceiving individual. Additionally, it scrutinizes the image interpretation process, drawing parallels to text interpretation and highlighting the influence of personal biases and individuality in medical practice. By revisiting Husserl's conceptualization of "image consciousness" and introducing the notion of "image theme", the paper seeks to establish a theoretical framework for making sense of images within the context of technological interpretation. A key objective is to enhance the phenomenology of technology through a systematic analysis of medical imaging diagnosis, contributing to an expanded epistemological foundation for medical practice. The article recognizes that the construction of medical knowledge incorporates subjective elements, especially within a historical context. The interpretation of images involves both instrumental and expert interpretation, with human subjectivity playing a crucial role. The article asserts that human creativity and conscious engagement are indispensable in interpreting all medical images.

Predicting rice diseases using advanced technologies at different scales: present status and future perspectives

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Imaging techniques for studying solid dosage formulation: Principles and applications

Classic methods for evaluating the disintegration and dissolution kinetics of solid dosage forms are no longer sufficient to meet the growing demands in the pharmaceutical field. Hence, scientists have turned to imaging techniques and computer technology to develop innovative visualization methods. These methods allow for a visual understanding of the disintegration or dissolution process and offer valuable insights into the drug release kinetics. This article aims to provide an overview of the commonly used imaging techniques and their applications in studying the disintegration or dissolution of solid dosage forms. Therefore, imaging presents a novel and alternative approach to understanding the mechanisms of disintegration and dissolution in the formulation study of solid dosages.

Validation of red dichromatic imaging score (RDI score) to evaluate the severity of ulcerative colitis

BACKGROUND

Red dichromatic imaging (RDI) is a new imaging technology that has been closely correlated with the activity index of ulcerative colitis (UC). Although the RDI score has been developed no study has validated a correlation between the RDI score and the activity index of UC. Therefore, this study aims to validate the RDI score prospectively.

METHODS

A total of 34 patients with UC in whom colonoscopy was scheduled between May 2019 and October 2021 at our hospital were enrolled prospectively. MES, UCEIS, RDI scores, and Nancy index were evaluated in a blinded fashion. We evaluated the correlation between RDI and WLI scores using still images with histology.

RESULTS

We analyzed 191 sites of colorectum. RDI score showed the positive correlation with UCEIS (r = 0.74 P < 0.0001) and MES (r = 0.78 P < 0.0001). RDI score also showed the positive correlation with Nancy index (r = 0.63 P < 0.0001). RDI score was more strongly correlated with Nancy index than UCEIS (r = 0.51) and MES (r = 0.48).

CONCLUSIONS

When comparing still images of RDI and WLI scores, we showed RDI had a higher correlation to histology than WLI.

CLINICAL TRIAL ID

The University Hospital Medical Information Network (UMIN000041750).

Recent technology for food and beverage quality assessment: a review

Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.

Brains exposed: How new imaging technology reconfigures expertise coordination in neurosurgery

This study explores the consequences of the uptake of novel medical technology for expertise coordination among specialists. Building on a 40-month field study, we show how the introduction of iMRI triggered a significant change in temporal, interactional and role dimensions of coordination. We trace the origin of those changes to the novel role the images started to play in surgery: iMRI generated new forms of visibility of the brain (offering real-time, more precise imaging) and new forms of ambiguity for clinical action (representing the brain undergoing manipulation). In response to this dual nature of images, specialists reconfigured how they engage with each other: aligning temporal rhythms of their corresponding work practices, shifting their interactions to focus more on dialog and synchronous mutual exploration of images, and delegating increased weight to radiological judgement for guiding immediate surgical action. We discuss the implications of our findings for research on expertise coordination and on the consequences of novel medical technologies for situated practice.

Computer assisted orthopaedic surgery: Past, present and future

Computer technology is ubiquitous and relied upon in virtually all professional activities including neurosurgery, which is why it is surprising that it is not the case for orthopaedic surgery with fewer than 5% of surgeons using available computer technology in their procedures. In this review, we explore the evolution and background of Computer Assisted Orthopaedic Surgery (CAOS), delving into the basic principles behind the technology and the changes in the discussion on the subject throughout the years and the impact these discussions had on the field. We found evidence that industry had an important role in driving the discussion at least in knee arthroplasty-a leading field of CAOS-with the ratio between patents and publications increased from approximately 1:10 in 2004 to almost 1:3 in 2014. The adoption of CAOS is largely restrained by economics and ergonomics with sceptics challenging the accuracy and precision of navigation during the early years of CAOS moving to patient functional improvements and long term survivorship. Nevertheless, the future of CAOS remains positive with the prospect of new technologies such as improvements in image-guided surgery, enhanced navigation systems, robotics and artificial intelligence.

Novel imaging and related techniques for studies of diseases of the central nervous system: a review

Imaging technologies for the analysis of the central nervous system are rapidly developing. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging, tracer-based magnetic resonance imaging, CLARITY technology and optogenetics can be used to visualize small molecules in brain tissues, the interstitial system of the brain and neuronal circuits in whole-brain samples. These tools serve as powerful technical means to explore the mechanisms underlying disease models and to evaluate the effects of drugs. Here, we review the constituting principles of these imaging techniques and describe their applications in the field of neuroscience.

Performance of common imaging techniques vs serum biomarkers in assessing fibrosis in patients with chronic hepatitis B: A systematic review and meta-analysis

BACKGROUND

Noninvasive biomarkers have been developed to predict hepatitis B virus (HBV) related fibrosis owing to the significant limitations of liver biopsy. Both serum biomarkers and imaging techniques have shown promising results and may improve the evaluation of liver fibrosis. However, most of the previous studies focused on the diagnostic effects of various imaging techniques on fibrosis in all chronic liver diseases.

AIM

To compare the performance of common imaging methods and serum biomarkers for prediction of significant fibrosis caused only by HBV infection.

METHODS

A systematic review was conducted on the records available in PubMed, EMBASE, and the Cochrane Library electronic databases until December 2018. We systematically assessed the effectiveness of two serum biomarkers and three imagine techniques in predicting significant fibrosis solely caused by HBV infection. The serum biomarkers included aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on the 4 factors (FIB-4). The three imaging techniques included acoustic radiation force impulse (ARFI), FibroScan, and magnetic resonance elastography (MRE). Three parameters, the area under the summary receiver operating characteristic curve (AUSROC), the summary diagnostic odds ratio, and the summary sensitivity and specificity, were used to examine the accuracy of all tests for liver fibrosis.

RESULTS

Out of 2831 articles evaluated for eligibility, 204 satisfied the predetermined inclusion criteria for this current meta-analysis. Eventually, our final data contained 81 studies. The AUSROCs of serum biomarkers of APRI and FIB-4 were both 0.75. For imaging techniques (ARFI, FibroScan, and MRE), the areas were 0.89, 0.83, and 0.97, respectively. The heterogeneities of ARFI and FibroScan were statistically significant (I² > 50%). The publication bias was not observed in any of the serum biomarkers or imaging methods.

CONCLUSION

These five methods have attained an acceptable level of diagnostic accuracy. Imaging techniques, MRE in particular, demonstrate significant advantages in accurately predicting HBV-related significant fibrosis, while serum biomarkers are admissible methods.

Radiation dose reduction utilizing noise reduction technology during uterine artery embolization: a pilot study

OBJECTIVE

To assess the radiation dose reduction during uterine artery embolization utilizing dose reduction technology.

METHODS

A total of 58 women underwent uterine artery embolization. A total of 26 procedures were performed in a standard fluoroscopy suite; 32 procedures were performed utilizing a novel imaging platform. Radiation dose data and acquisition parameters were compared.

RESULTS

The new platform provided significant reduction in the median radiation dose (P<.001): from 389Gy cm(2) to 145Gy cm(2). There were no differences between the groups with regard to acquisition parameters.

CONCLUSION

The new imaging platform provided a 61% dose reduction during uterine artery embolization without a significant change in acquisition parameters.

A retrieval-based computer-aided diagnosis system for the characterization of liver lesions in CT scans

RATIONALE AND OBJECTIVES

To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access.

MATERIALS AND METHODS

The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The system's performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver.

RESULTS

The CADx system's leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the system's performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data.

CONCLUSIONS

The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular.

Progress in the diagnosis of ileocecal lesions

The ileocecal area includes the ileocecal valve, terminal ileum, cecum, appendix, and top ascending colon. Since this area may be affected by pathologic conditions that are either common throughout the gastrointestinal system or exclusive to this area, a great variety of intestinal diseases may arise in this region, making the diagnosis of ileocecal lesions difficult. In clinic, the diagnosis of ileocecal lesions depends on the combination of disease history, clinical features, imaging and endoscopic findings. The development of advanced imaging and endoscopy technology has greatly increased the detection rate of ileocecal lesions. In this review, we discuss the use of imaging and endoscopic examinations for evaluation of ileocecal area to help ensure accurate diagnosis and appropriate treatment.

Technology of in vivo two-dimension multi-voxel 1H magnetic resonance spectroscopy for rabbit liver VX2 tumor

AIM: To investigate the best techniques of in vivo two-dimension multi-voxel ¹H magnetic resonance spectroscopy (2D ¹H-MRS) for rabbit liver VX2 tumor.

METHODS: The liver of 8 New Zealand white rabbits was implanted directly and respectively with VX2 tumor lump after abdominal cavity was opened. 2D ¹H-MRS acquisition in vivo and unenhanced MRI was performed respectively from the 2^nd week to 4^th week after VX2 tumor was implanted. With knee coil, in vivo 2D ¹H-MRS acquisitions were performed respectively with different TR, different TE and different NEX at 1.5 T MR scanner when other parameters were the same. The distinction between groups was analyzed by SPSS11.0 with baseline and signal-noise ratio (SNR).

RESULTS: From the qualified MRS spectrum, there were up to 6 peaks which could be identified: methyl lipids (Lip1), methylene lipids (Lip2), methylene lipids with double carbon bond (Lip3), glutamine and glutamate complex (Glx), Choline (Cho), and glycogen and glucose complex (Glyu). Baseline and SNR had no significant differences between TR = 1000 ms, 2000 ms and 3000 ms. Baseline had no significant difference between TE = 30 ms and 144 ms. Except that SNR of Glx with 30 ms in TE was higher than that with 144 ms in TE (1.95 ± 0.36 vs 1.24 ± 0.26, P < 0.05), SNR of other metabolites were similar. With NEX increasing, the distinctions of baseline between NEX = 4, 8 and 16 were significant (χ² = 10.000, P < 0.01). SNR of all metabolites increased when NEX was increased gradually. Both of baseline and SNR were the best when NEX was 16.

CONCLUSION: It's practical of in vivo two-dimension multi-voxel ¹H-MRS on the rabbit liver VX2 tumor by a 1.5 T MR scanner. Immobilization, pre-scan (reaching FWHM ≤ 10 Hz and WS ≥ 98%), knee coil, TR = 1000 ms, TE = 30 ms and NEX = 16 may be the best to acquire highly qualified spectra.