JMA journal最新文献

Introduction: Excessive workload among medical residents remains a social issue, particularly in Japan. The government requires management of overtime work in health institutions. Among young healthcare workers, the demand for sustainable work-life balance is increasing. This study evaluated the current workload and work allocation of postgraduate residents using a mobile application.

Methods: A cross-sectional study including postgraduate trainees from three major teaching hospitals was conducted in 2021 using a mobile application. The residents recorded their work (direct patient care, indirect patient care, education, research, administration, personal time, and others) using the application. The data were descriptively analyzed.

Results: A total of 69 residents participated in the survey. Their mean working hours was 11 h and 45 min, and their mean sleep time was 6 h and 18 min. The proportions of work allocation time by category were 35.5% for direct patient care; 35.5%, indirect patient care; 10.1%, personal time; 9.4%, education; 8.6%, administration; and 1%, research.

Conclusions: The development of a mobile application enabled us to measure the residents' workload and work allocation. The time spent on direct and indirect patient care increased over a decade, whereas the time spent on educational activities and research remained limited.

Introduction: The integration of artificial intelligence (AI) into medical practices has transformed fields like gastroenterological surgery. AI predicts patient prognoses using clinical and pathological data and develops technologies that create three-dimensional (3D) models for surgical simulations, thereby enhancing surgical precision and care quality.

Methods: At our facility, AI-driven diagnostic and treatment systems have been developed under the "Strategic Innovation Creation Program" by the Cabinet Office. Our research focuses on perioperative care by constructing 3D models from preoperative imaging data to develop surgical support systems for preoperative simulations and navigation during surgery. Additionally, we use deep learning to predict disease progression and complications and natural language processing to analyze electronic medical records to predict postoperative complications.

Results: AI-based surgical support systems effectively convert two-dimensional imaging data into 3D models, thereby improving surgical precision. Predictive models for disease progression and complications developed using deep learning have high accuracy. AI applications in diagnostic imaging enable early detection and improved treatment planning. AI-based tools for informed consent and patient support enhance patient understanding and satisfaction.

Conclusions: AI revolutionizes medical practices by improving diagnostic accuracy, surgical precision, and patient outcomes. Future projects will integrate remote diagnostic and treatment planning; leverage AI for comprehensive, high-quality care; and support work-style reforms for healthcare professionals. Advancements in AI will overcome current medical challenges and enhance the communication between physicians and patients.

Introduction: This prospective, multicenter, single-arm Phase II trial investigates the feasibility and the safety of tailored axillary surgery (TAS) in patients with clinically node-positive breast cancer who are undergoing upfront surgery. The trial aims to establish the criteria for safely omitting axillary lymph node dissection (ALND) in these cases, potentially shifting breast cancer management by minimizing surgical complications and preserving the patients' quality of life (QOL).

Methods: The study includes patients who were diagnosed with invasive breast cancer, particularly those with limited metastatic lymph nodes. The primary objective of this work is to determine the specific combination of clinical and pathological factors that would result in a non-TAS lymph node metastasis proportion of less than 10%. The secondary objectives include assessing the identification rate of the metastatic lymph nodes, the incidence of upper limb lymphedema, and the QOL measures.

Results: The results will identify the patient eligibility criteria for the Phase III TAS trial, potentially allowing the omission of ALND in selected patients. This may lead to reduced surgical complications and better preservation of the QOL of patients with breast cancer.

Conclusions: The trial's outcome will contribute to the development of the criteria for safely omitting ALND in certain patients with clinically node-positive breast cancer. This approach aims to enhance breast cancer management by reducing surgical burden and improving the patient outcomes.jRCTs: 061220113.

This review outlines the implementation of artificial intelligence (AI) into colonoscopy procedures which includes its history, processes, and challenges. We highlight the importance of the collaborative effort between medical and computer science researchers in the development of AI tools in colonoscopy, particularly focusing on the roles of computer-aided detection (CADe) and computer-aided characterization (CADx) in a real time analysis of colonoscopy videos. Some of the proposed technologies are considered to improve the important clinical outcomes of patients such as adenoma detection rate in colonoscopy. Regulatory approval is considered mandatory before introducing AI tools into the market owing to the potential risks associated with the introduction of AI tools in healthcare. We share the experience of obtaining regulatory approval for EndoBRAIN in Japan, emphasizing the challenges in establishing examination criteria and performance levels at the period. Reimbursement is also identified as necessary for the widespread adoption of medical innovation. With the introduction of reimbursement for a CADe tool in Japan in 2024, we expect to accelerate implementation of AI in colonoscopy in general. Despite regulatory approval and reimbursement, concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy. Questions about its impact on cancer prevention, healthcare burden, patient acceptance, and effectiveness across different populations remain unsolved. The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice. While it is always exciting to strive for medical innovation, ensuring rigorous evaluation to optimize patient care is mandatory to improve the quality of health and society.

In Japan, mammography is commonly used for breast cancer screening. However, the mortality rate has not decreased, possibly due to the low screening uptake and the high prevalence of dense breast tissue among Japanese women, which reduces mammography's effectiveness. A recent prospective study in Japan, J-START, demonstrated that combining mammography with ultrasonography increases detection rates and reduces the incidence of interval cancers, highlighting the significance of ultrasound examinations. Artificial Intelligence (AI) technologies, particularly in machine learning and deep learning, offer promising solutions to enhance the accuracy and efficiency of breast ultrasound diagnostics. This review explores AI's current capabilities in breast ultrasound imaging, emphasizing key advancements in breast lesion detection and diagnosis. Additionally, we introduce AI-based breast ultrasound diagnostic support programs approved by the Pharmaceuticals and Medical Devices Agency, which include programs for detecting lesion candidate regions and diagnosing the necessity of further examination based on detected lesion candidates. These AI tools are expected to improve diagnostic accuracy and efficiency. While AI holds significant promise, several challenges remain. It is essential for physicians to oversee its use responsibly, as there are concerns regarding patient acceptance and environmental impact. This review underscores the revolutionary potential of AI in breast cancer diagnostics and emphasizes the importance of ongoing research and development to overcome existing limitations.

In recent years, artificial intelligence (AI) research in the medical field has been actively conducted owing to the evolution of algorithms, such as deep learning, and advances in hardware, such as graphics processing units, and some such medical devices have been used in clinics. AI research in obstetrics and gynecology has also increased. This review discusses the latest studies in each field. In the perinatal field, there are reports on cardiotocography, studies on the diagnosis of fetal abnormalities using ultrasound scans, and studies on placenta previa using magnetic resonance imaging (MRI). In the reproduction field, numerous studies have been conducted on the efficiency of assisted reproductive technology as well as selection of suitable oocyte and good embryos. As regards gynecologic cancers, there are many reports on diagnosis using MRI and prognosis prediction using histopathology in cervical cancer, diagnosis using hysteroscopy and prediction of molecular subtypes based on histopathology in endometrial cancer, and diagnosis using MRI and ultrasound as well as prediction of anticancer drug efficacy in ovarian cancer. However, concerns related to AI research include handling of personal information, lack of governing laws, and transparency. These must be addressed to facilitate advanced AI research.

Introduction: Organoids are miniature organs developed through technology. Kidney organoids that originate from human inducible pluripotent stem cells (iPSCs) were developed to recreate renal diseases. However, it is impossible to simultaneously produce kidney organoids from iPSCs of multiple individuals and in a single medium. We herein report the development of adult renal tubular organoids, namely, "tubuloids," from primary renal epithelial cells from multiple human individuals in a single medium.

Methods: Kidneys from eight patients who underwent nephrectomy due to malignancy were sectioned, and primary renal epithelial tubule cells were cultured; four had normal kidney function, and four had mild chronic kidney disease (CKD). Growth factors and Matrigel were added to the primary culture.

Results: Primary cultured renal epithelial cells from normal kidneys exhibited a fine and swollen epithelial appearance, whereas those from kidneys with mild CKD were smaller and slightly elongated. Growth was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal and mild CKD tubuloids was not obvious by day 5. Both tubuloid types had comparable sizes by day 21.

Conclusions: Renal tubular organoids can be developed simultaneously and in a single medium. Our method is expected to be used as a human pathological model.

Introduction: Hypophosphatasia has been reported to develop nephrocalcinosis, renal stone, and chronic kidney failure. We investigated their renal impairments in the adults with hypophosphatasia to know the phenotype-genotype correlation.

Methods: We subjected 11 patients with hypophosphatasia who were diagnosed by chance in the routine medical health checkup. Most cases had past history of fracture. Bone mineral density showed low or lower normal limit.

Results: Four of six patients also had high levels of ionized Ca. In subjected six cases, four showed high urinary Ca excretion. Nephrocalcinosis is found in five cases even if the symptoms of hypophosphatasia are mild. Four out of five patients with a mutation of c.1559del in ALPL had nephrocalcinosis and/or kidney stones. One patient already developed hydronephrosis. One of six patients with other mutations showed nephrocalcinosis.

Conclusions: The phenotype-genotype correlation between renal impairment and c.1559del of ALPL gene was suggested.

Background: Aspiration pneumonia is a prevalent condition, and understanding the risk factors associated with discontinuation of oral intake upon discharge is crucial. This study aimed to identify such factors, thereby providing valuable insights for optimizing the use of limited healthcare resources and enhancing patient and family care.

Methods: In this scoping review, data were collected through ICHUSHI using the search formula "Pneumonia-Aspiration/Thesaurus or Aspiration Pneumonia/All) and (Prognosis/Thesaurus or Prognosis/All)." The inclusion criteria encompassed Japanese patients hospitalized for aspiration pneumonia, with a clear outcome focused on the availability of oral intake. The exclusion criteria included text unavailability, studies from foreign countries, and cases involving not hospitalized patients. The risk of bias for each study was assessed using the Newcastle-Ottawa scale.

Results: Using this search formula, 1,646 articles were initially identified, culminating in the inclusion of six articles for analysis. The investigation revealed five significant risk factors: social status (age and gender), nutritional status (body mass index, Controlling Nutritional Status score, serum albumin, Basal Energy Expenditure, and low body weight), physical swallowing function (ambulatory ability before admission, Food Intake LEVEL scale (FILS), admission origin, bedridden status, Penetration-Aspiration scale, presence of residual pharyngeal material, and Basal Index), pneumonia severity (A-DROP score, a classification tool incorporating age, dehydration, oxygen demand, impaired consciousness, and hypotension), and comorbidities (pneumonia, dementia, mental illness, malignancy, chronic lower respiratory tract involvement, and renal failure).

Conclusions: This scoping review identified five key risk factors associated with oral intake discontinuation upon discharge in patients hospitalized for aspiration pneumonia, providing valuable evidence for future clinical practice.