The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

Mohsin, Syed Naveed; Gapizov, Abubakar; Ekhator, Chukwuyem; Ain, Noor  U; Ahmad, Saeed; Khan, Mavra; Barker, Chad; Hussain, Muqaddas; Malineni, Jahnavi; Ramadhan, Afif; Halappa Nagaraj, Raghu

doi:10.7759/cureus.44374

Cited by 14 publications

(8 citation statements)

References 44 publications

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“…Also, careful interpretation of the medical images still requires experts' oversight, to ensure images are not misinforming users [35,42]. One capability of AI-models is their ability to acquire knowledge and improve performance through increased exposure to data, therefore, IT experts could enhance current and future AI-models' training, emphasizing variety of accurate medical images datasets and improving algorithms to enhance generated image's reliability and usefulness in medical education [49,50].…”

Section: Discussionmentioning

confidence: 99%

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Temsah,

Alhuzaimi,

Almansour

et al. 2024

J Med Syst

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Arti cial Intelligence (AI), particularly AI-Generated Imagery, holds the capability to transform medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL•E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. The study involved generating a total of 110 images for normal human heart and 20 common CHDs through DALL•E 3. Then, 33 healthcare professionals systematically assessed these AI-generated images by variable levels of healthcare professionals (HCPs) using a developed framework to individually assess each image anatomical accuracy, in-picture text usefulness, image appeal to medical professionals and the potential to use the image in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be signi cantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were signi cant challenges identi ed in image generation. These ndings suggest adopting a cautious approach in integrating AI imagery in medical education, emphasizing the need for rigorous validation and interdisciplinary collaboration. The study advocates for future AI-models to be ne-tuned with accurate medical data, enhancing their reliability and educational utility.

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Section: Discussionmentioning

confidence: 99%

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Temsah,

Alhuzaimi,

Almansour

et al. 2024

J Med Syst

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Section: Discussionmentioning

confidence: 99%

“…[28] The integration of artificial intelligence into clinical practice may have profound implications, such as tailored treatment planning, costeffectiveness, and further enhancing patient care. [29] Analysis of outcomes, according to the currently available scoring systems for congenital heart surgery, is not new in Türkiye. Some centers use their own database for the comparison of their results with the current world statistics as an effort for quality improvement in congenital heart surgery.…”

Section: Ethics Committee Approvalmentioning

confidence: 99%

Second harvest of Congenital Heart Surgery Database in Türkiye: Current outcomes

Erek,

Başgöze,

Yıldız

et al. 2024

Turk Gogus Kalp Dama

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Background: This second harvest of the Congenital Heart Surgery Database intended to compare current results with international databases. Methods: This retrospective study examined a total of 4007 congenital heart surgery procedures from 15 centers in the Congenital Heart Surgery Database between January 2018 and January 2023. International diagnostic and procedural codes were used for data entry. STAT (Society of Thoracic Surgeons and European Association for Cardiothoracic Surgery) mortality scores and categories were used for comparison of the data. Surgical priority status was modified from American Society of Anesthesiologist guidelines. Centers that sent more than 5 cases to the database were included to the study. Results: Cardiopulmonary bypass and cardioplegic arrest were performed in 2,983 (74.4%) procedures. General risk factors were present in 22.6% of the patients, such as genetic anomaly, syndrome, or prematurity. Overall, 18.9% of the patients had preoperative risk factors (e.g., mechanical ventilation, renal failure, and sepsis). Of the procedures, 610 (15.2%) were performed on neonates, 1,450 (36.2%) on infants, 1,803 (45%) on children, and 144 (3.6%) on adults. The operative timing was elective in 56.5% of the patients, 34.4% were urgent, 8% were emergent, and 1.1% were rescue procedures. Extracorporeal membrane oxygenation support was used in 163 (4%) patients, with a 34.3% survival rate. Overall mortality in this series was 6.7% (n=271). Risk for mortality was higher in patients with general risk factors, such as prematurity, low birth weight neonates, and heterotaxy syndrome. Mortality for patients with preoperative mechanical ventilation was 17.5%. Pulmonary hypertension and preoperative circulatory shock had 11.6% and 10% mortality rates, respectively. Mortality for patients who had no preoperative risk factor was 3.9%. Neonates had the highest mortality rate (20.5%). Intensive care unit and hospital stay time for neonates (median of 17.8 days and 24.8 days, respectively) were also higher than the other age groups. Infants had 6.2% mortality. Hospital mortality was 2.8% for children and 3.5% for adults. Mortality rate was 2.8% for elective cases. Observed mortality rates were higher than expected in the fourth and fifth categories of the STAT system (observed, 14.8% and 51.9%; expected, 9.9% and 23.1%; respectively). Conclusion: For the first time, outcomes of congenital heart surgery in Türkiye could be compared to the current world experience with this multicenter database study. Increased mortality rate of neonatal and complex heart operations could be delineated as areas that need improvement. The Congenital Heart Surgery Database has great potential for quality improvement of congenital heart surgery in Türkiye. In the long term, participation of more centers in the database may allow more accurate risk adjustment.

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“…Clinical decision support: AI is a valuable tool for clinicians by providing decision support. It offers evidence-based recommendations and insights derived from vast datasets, helping doctors make informed decisions about diagnostics, treatments, and prognoses [68]. Rapid diagnostics and triage: AI algorithms can automate the triage process by quickly analyzing symptoms and patient data.…”

Section: Ai-innovations: Transforming Diverse Industriesmentioning

confidence: 99%

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Aldoseri,

Al-Khalifa,

Hamouda

2024

Sustainability

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Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

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The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases

Cited by 14 publications

References 44 publications

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases

Second harvest of Congenital Heart Surgery Database in Türkiye: Current outcomes

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

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