Artificial Intelligence Clinical Trial
Official title:
Evaluation of Comfort Behavior Levels of Newborns With Artificial Intelligence Techniques
This study; It will be carried out with the aim of developing the artificial intelligence method, which allows automatic determination of comfort levels of newborns.
Status | Not yet recruiting |
Enrollment | 1000 |
Est. completion date | September 2021 |
Est. primary completion date | December 2020 |
Accepts healthy volunteers | No |
Gender | All |
Age group | N/A to 28 Days |
Eligibility |
Inclusion Criteria: - Parents' acceptance of their baby's participation in the study - Having a baby born at 24-42 weeks Exclusion Criteria: - Having a baby born before 24 weeks of gestation or after 42 weeks of gestation. - Has received analgesic, muscle relaxant or sedative drug treatment that may affect comfort and behavior (last 24 hours) - Newborn has serious neurological damage |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Eskisehir Osmangazi University |
Ambuel B, Hamlett KW, Marx CM, Blumer JL. Assessing distress in pediatric intensive care environments: the COMFORT scale. J Pediatr Psychol. 1992 Feb;17(1):95-109. — View Citation
Arroyo-Novoa CM, Figueroa-Ramos MI, Puntillo KA, Stanik-Hutt J, Thompson CL, White C, Wild LR. Pain related to tracheal suctioning in awake acutely and critically ill adults: a descriptive study. Intensive Crit Care Nurs. 2008 Feb;24(1):20-7. Epub 2007 Au — View Citation
Arslan, H., & Konuk Sener, D. (2009). Stigma, spiritüalite ve konfor kavramlarinin Meleis' in kavram gelistirme sürecine göre irdelenmesi. Maltepe Üniversitesi Hemsirelik Bilim ve Sanati Dergisi, 2(1): 51-58.
Atalay, M., & Çelik, E. (2017). Büyük veri analizinde yapay zekâ ve makine ögrenmesi uygulamalari. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22): 155-172.
Aydin, S.E. (2017). Yapay zeka teknolojisi (yapay zekalarin dünü bugünü yarini), Yüksek lisans tezi, Çukurova Üniversitesi, Adana.
Bodur, G., & Kaya, H. (2017). Hemsirelerin gözüyle gelecek: 2050'li yillar. Ege Üniversitesi Hemsirelik Fakültesi Dergisi, 33 (1): 27-38.
Çimen, Ü. (2016). Solunum seslerinin yapay zekâ ortaminda siniflandirilmasi, Yüksek Lisans Tezi, Afyon Kocatepe Üniversitesi Fen Bilimleri Enstitüsü, Afyonkarahisar.
Çinar Yücel S. (2011). Kolcaba'nin konfor kurami. Ege Üniversitesi Hemsirelik Yüksek Okulu Dergisi, 27: 79-88.
Coughlin M, Gibbins S, Hoath S. Core measures for developmentally supportive care in neonatal intensive care units: theory, precedence and practice. J Adv Nurs. 2009 Oct;65(10):2239-48. doi: 10.1111/j.1365-2648.2009.05052.x. — View Citation
Freire NB, Garcia JB, Lamy ZC. Evaluation of analgesic effect of skin-to-skin contact compared to oral glucose in preterm neonates. Pain. 2008 Sep 30;139(1):28-33. doi: 10.1016/j.pain.2008.02.031. Epub 2008 Apr 22. — View Citation
Hunter, J. (2010). Therapeutic positioning: neuromotor, physiologic, and sleep implications. Developmental care of newborns and infants. A guide for health professionals. 2nd edn. Glenview, IL: NANN: 283-312.
Ista E, van Dijk M, Tibboel D, de Hoog M. Assessment of sedation levels in pediatric intensive care patients can be improved by using the COMFORT "behavior" scale. Pediatr Crit Care Med. 2005 Jan;6(1):58-63. — View Citation
Kahraman, A., Basbakkal, Z., & Yalaz, M. (2014). Yenidogan konfor davranis ölçegi'nin Türkçe geçerlik ve güvenirligi. Uluslararasi Hakemli Hemsirelik Arastirmalari Dergisi, 1(2).
Karabacak, Ü., & Acaroglu, R. (2011). Konfor kurami. Maltepe Üniversitesi Bilim ve Sanat Dergisi, 4(1): 197-202.
Karakaplan, S., & Yildiz, H. (2010). Dogum sonu konfor ölçegi gelistirme çalismasi. Maltepe Üniversitesi Hemsirelik Bilim ve Sanati Dergisi, 3(1): 55-65.
Kaya, U., Yilmaz, A., & Dikmen, Y. (2019). Saglik alaninda kullanilan derin ögrenme yöntemleri. Avrupa Bilim ve Teknoloji Dergisi, (16): 792-808.
Kolcaba K, DiMarco MA. Comfort Theory and its application to pediatric nursing. Pediatr Nurs. 2005 May-Jun;31(3):187-94. Review. — View Citation
Kolcaba K, Tilton C, Drouin C. Comfort Theory: a unifying framework to enhance the practice environment. J Nurs Adm. 2006 Nov;36(11):538-44. — View Citation
Küçük Alemdar, D., & Güdücü Tüfekçi, F. (2015). Prematüre bebek konfor ölçegi'nin Türkçe geçerlilik ve güvenilirligi. Hemsirelikte Egitim ve Arastirma Dergisi, 12 (2): 142-148.
Kuguoglu, S., & Karabacak, Ü. (2008). Genel konfor ölçeginin Türkçe'ye uyarlanmasi. I.Ü.F.N. Hemsirelik Dergisi, 16 (61): 16-23.
Losacco V, Cuttini M, Greisen G, Haumont D, Pallás-Alonso CR, Pierrat V, Warren I, Smit BJ, Westrup B, Sizun J; ESF Network. Heel blood sampling in European neonatal intensive care units: compliance with pain management guidelines. Arch Dis Child Fetal Ne — View Citation
Mathai S, Natrajan N, Rajalakshmi NR. A comparative study of nonpharmacological methods to reduce pain in neonates. Indian Pediatr. 2006 Dec;43(12):1070-5. — View Citation
Mijwel, M. M. (2015). History of artificial intelligence. Computer science, college of science, 1-6.
Sönmez, D. (2009). Pediatrik yogun bakim ünitesinde endotrakeal aspirasyon agrisinin degerlendirilmes, Yüksek lisans tezi, Marmara Üniversitesi Saglik Bilimleri Enstitüsü, Istanbul.
Terzi, B., Kaya, N. (2017). Konfor kurami ve analizi. Anadolu Hemsirelik ve Saglik Bilimleri Dergisi, 20: 1.
Tutar Güven, S., & Isler Dalgiç, A. (2017). Prematüre yenidoganlar için gelistirilmis bireysellestirilmis destekleyici gelisimsel bakim programi. Uluslararasi Hakemli Kadin Hastaliklari ve Anne Çocuk Sagligi Dergisi, 9.
Uga E, Candriella M, Perino A, Alloni V, Angilella G, Trada M, Ziliotto AM, Rossi MB, Tozzini D, Tripaldi C, Vaglio M, Grossi L, Allen M, Provera S. Heel lance in newborn during breastfeeding: an evaluation of analgesic effect of this procedure. Ital J Pe — View Citation
van Dijk M, Roofthooft DW, Anand KJ, Guldemond F, de Graaf J, Simons S, de Jager Y, van Goudoever JB, Tibboel D. Taking up the challenge of measuring prolonged pain in (premature) neonates: the COMFORTneo scale seems promising. Clin J Pain. 2009 Sep;25(7) — View Citation
Yildirim, T. (2019). Saglikta dönüsüm ve yapay zekânin pediatriye yansimalari. Karadeniz Pediatri Günleri Kongre Özet Kitabi, 2 (1): 1-97.
* Note: There are 29 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Images of the newborn | Images will be taken to transfer behaviors of newborns to the computer. Attention will be paid to making face and body movements visible while taking the image.The camera will take 12 hours of view of each newborn.However, this period can be shortened as a result of preliminary work. | 12 hours for each newborn | |
Secondary | Artificial intelligence techniques | The images of the newborn will be transferred to the computer. The images will be processed by a specialist on computer using artificial intelligence techniques. | Approximately 1 week for each newborn |
Status | Clinical Trial | Phase | |
---|---|---|---|
Completed |
NCT04589078 -
Polyp REcognition Assisted by a Device Interactive Characterization Tool - The PREDICT Study
|
||
Completed |
NCT03857438 -
Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
|
||
Completed |
NCT04735055 -
Artificial Intelligence Prediction for the Severity of Acute Pancreatitis
|
||
Not yet recruiting |
NCT05452993 -
Screening for Diabetic Retinopathy in Pharmacies With Artificial Intelligence Enhanced Retinophotography
|
N/A | |
Completed |
NCT05687318 -
A Clinical Trial of the Effectiveness and Safety of Software Assisting Diagnose the Intestinal Polyp Digestive Endoscopy by Analysis of Colonoscopy Medical Images From Electronic Digestive Endoscopy Equipment
|
N/A | |
Recruiting |
NCT06051682 -
Optimization of the Diagnosis of Bone Fractures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor.
|
N/A | |
Not yet recruiting |
NCT06039917 -
Effect of the Automatic Surveillance System on Surveillance Rate of Patients With Gastric Premalignant Lesions
|
N/A | |
Not yet recruiting |
NCT06362629 -
AI App for Management of Atopic Dermatitis
|
N/A | |
Recruiting |
NCT06059378 -
Real-life Implementation of an AI-based Optical Diagnosis
|
N/A | |
Recruiting |
NCT06164002 -
A I in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With 2 Ceramic Materials
|
N/A | |
Completed |
NCT05517889 -
Repeatability and Stability of Healthy Skin Features on OCT
|
||
Completed |
NCT04816981 -
AI-EBUS-Elastography for LN Staging
|
N/A | |
Completed |
NCT05006092 -
Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)
|
N/A | |
Recruiting |
NCT04535466 -
Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics
|
||
Enrolling by invitation |
NCT04719117 -
Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
|
||
Completed |
NCT04399590 -
Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
|
||
Recruiting |
NCT04126265 -
Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps
|
N/A | |
Recruiting |
NCT06255808 -
Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor
|
||
Recruiting |
NCT04131530 -
Automatic Evaluation of Inflammation Activity in Ulcerative Colitis Using pCLE With Artificial Intelligence
|
||
Recruiting |
NCT04598997 -
Artificial Intelligence With DEep Learning on COROnary Microvascular Disease
|