Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

Purpose: This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods: PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results: We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.

The HeartWare Ventricular Assist Device (HVAD) was widely used for mechanical circulatory support in patients with end-stage heart failure. However, there have been reports of a critical issue with HVAD pumps failing to restart, or experiencing delays in restarting, after being stopped. This case report describes 2 instances of HVAD failure-to-restart during heart transplantation surgery and routine outpatient care. Despite multiple attempts to restart the pump using various controllers and extensions, the HVAD failed to restart, triggering a hazard alarm for pump stoppage. In one case, the patient survived after receiving a heart transplantation, while in the other, the patient died immediately following the controller exchange. These cases highlight the rare but life-threatening complication of HVAD failure-to-restart, underscoring the importance of awareness among clinicians, patients, and caregivers, and adherence to the manufacturer’s guidelines and recommendations for HVAD management.

Purpose: The purpose of this study was to investigate the concordance rate of PIK3CA mutations between primary and matched distant metastatic sites in patients with breast cancer and to verify whether there are differences in the frequency of PIK3CA hotspot mutations depending on the metastatic sites involved. Materials and Methods: Archived formalin-fixed paraffin-embedded (FFPE) specimens of primary breast and matched distant metastatic tumors were retrospectively obtained for 49 patients. Additionally, 40 archived FFPE specimens were independently collected from different breast cancer metastatic sites, which were limited to three common sites: the liver, brain, and lung. PIK3CA mutations were analyzed using droplet digital PCR, including hotspots involving exons 9 and 20. Results: After analysis of 49 breast tumors with matched metastasis sites, 87.8% showed concordance in PIK3CA mutation status. According to PIK3CA hotspot mutation testing in 89 cases of breast cancer metastatic sites, the proportion of PIK3CA mutations at sites of metastasis involving the liver, brain, and lung was 37.5%, 28.6%, and 42.9%, respectively, which did not result in statistical significance. Conclusion The high concordance of PIK3CA mutation status between primary and matched metastasis sites suggests that metastatic sites, regardless of the metastatic organ, could be considered sample sources for PIK3CA mutation testing for improved therapeutic strategies in patients with metastatic breast cancer.

The HeartWare Ventricular Assist Device (HVAD) was widely used for mechanical circulatory support in patients with end-stage heart failure. However, there have been reports of a critical issue with HVAD pumps failing to restart, or experiencing delays in restarting, after being stopped. This case report describes 2 instances of HVAD failure-to-restart during heart transplantation surgery and routine outpatient care. Despite multiple attempts to restart the pump using various controllers and extensions, the HVAD failed to restart, triggering a hazard alarm for pump stoppage. In one case, the patient survived after receiving a heart transplantation, while in the other, the patient died immediately following the controller exchange. These cases highlight the rare but life-threatening complication of HVAD failure-to-restart, underscoring the importance of awareness among clinicians, patients, and caregivers, and adherence to the manufacturer’s guidelines and recommendations for HVAD management.

The HeartWare Ventricular Assist Device (HVAD) was widely used for mechanical circulatory support in patients with end-stage heart failure. However, there have been reports of a critical issue with HVAD pumps failing to restart, or experiencing delays in restarting, after being stopped. This case report describes 2 instances of HVAD failure-to-restart during heart transplantation surgery and routine outpatient care. Despite multiple attempts to restart the pump using various controllers and extensions, the HVAD failed to restart, triggering a hazard alarm for pump stoppage. In one case, the patient survived after receiving a heart transplantation, while in the other, the patient died immediately following the controller exchange. These cases highlight the rare but life-threatening complication of HVAD failure-to-restart, underscoring the importance of awareness among clinicians, patients, and caregivers, and adherence to the manufacturer’s guidelines and recommendations for HVAD management.

The HeartWare Ventricular Assist Device (HVAD) was widely used for mechanical circulatory support in patients with end-stage heart failure. However, there have been reports of a critical issue with HVAD pumps failing to restart, or experiencing delays in restarting, after being stopped. This case report describes 2 instances of HVAD failure-to-restart during heart transplantation surgery and routine outpatient care. Despite multiple attempts to restart the pump using various controllers and extensions, the HVAD failed to restart, triggering a hazard alarm for pump stoppage. In one case, the patient survived after receiving a heart transplantation, while in the other, the patient died immediately following the controller exchange. These cases highlight the rare but life-threatening complication of HVAD failure-to-restart, underscoring the importance of awareness among clinicians, patients, and caregivers, and adherence to the manufacturer’s guidelines and recommendations for HVAD management.

EBV infection has been causally associated with incidence of many carcinomas. EBV-associated gastric carcinoma (EBVaGC) has been classified as a unique gastric carcinoma subset, suggesting EBV infection is related to the development of gastric cancer. In this study, general trends of EBVaGC studies for last half-decades were reviewed in several perspectives of clinical significance, virological importance and etiological interests. Throughout this comprehensive reviewing, new study trends of EBV and EBVaGC for next half-decades were suggested.
Epstein-Barr Virus Infections ; Herpesvirus 4, Human* ; Incidence ; Methylation ; Prognosis ; Stomach Neoplasms