Delivery Outcomes by AIDA (Artificial Intelligence Dystocia Algorithm) Analysis
NCT ID: NCT06664112
Last Updated: 2024-12-03
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
RECRUITING
Total Enrollment
1000 participants
Study Classification
OBSERVATIONAL
Study Start Date
2024-09-30
Study Completion Date
2026-06-30
Brief Summary
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Aims:
Primary aim is to investigate outcomes of eutocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).
Secondary aim is to investigate outcomes of dystocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).
Tertiary aim is to investigate of neonatal outcomes of eutocic labor: Apgar scores at 1 min, Apgar scores at 5 min.
Quaternary aim is to investigate of neonatal outcomes of dystocic labor: Apgar scores at 1 min, Apgar scores at 5 min.
Primary aim is to investigate outcomes of eutocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).
Secondary aim is to investigate outcomes of dystocic labor, evaluating intrapartum ultrasound parameters by AIDA method: Angle of progression (AoP), Asynclitism degree (AD), fetal head-symphysis distance (HSD), and midline angle (MLA).
Tertiary aim is to investigate of neonatal outcomes of eutocic labor: Apgar scores at 1 min, Apgar scores at 5 min.
Quaternary aim is to investigate of neonatal outcomes of dystocic labor: Apgar scores at 1 min, Apgar scores at 5 min.
Detailed Description
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One of the hardest processes to overcome during labor is the positioning of the fetal head during engagement and progression in the birth canal. These malpositions and malrotations are also common causes of dystocic labor and arrest of progression, which necessitates a surgical delivery. Traditionally, a vaginal digital examination is used to assess the fetal head position and engagement. The degree of fetal engagement and progression is delineated by different planes of the pelvis, which have values ranging from -5 (5 cm above the ischial spines) to +5 (5 cm below the ischial spines with the fetal head visible at the introitus). The head is considered engaged when the leading point of the skull touches the ischial spine plane; this is referred to as station 0. Moreover, when the parietal bone is the presenting feature, asynclitism is identified. When the front parietal bone manifests, the condition is identified as anterior asynclitism; when the posterior parietal bone presents, the condition is described as posterior asynclitism. The fetal head's minor "tilted" posture in the birth canal, however, is a result of the fetal head's physiological adjustment to the mother's pelvis throughout labor. Malposition and malrotation of the fetal head in the birth canal, characterized by significant asynclitism, might result in dystocia that necessitates surgical delivery. The 15% of people have asynclitism, with anterior asynclitism being more common than posterior asynclitism. The traditional classification of degree of fetal engagement and progression has been criticized as inaccurate and poorly reproducible. Furthermore, the failure of instrumental delivery, which may reach up to 10%. Errors in the diagnosis of the fetal head station can have major implications during labor and adverse perinatal outcomes, such as acidemia, fetal traumas, intracranial hemorrhages, and low Apgar scores after an emergency cesarean section for failed operative vaginal deliveries. Many researchers have proposed many ultrasonographic parameters, collected during labor and delivery, to evaluate the engagement, descent, and internal rotation of the fetal head in the birth canal. Thus, we used intrapartum ultrasound parameters, measured in all the women during labor and recorded to be measured by artificial intelligence and machine learning algorithms, called AIDA (Artificial Intelligence Dystocia Algorithm), which incorporates a human-in-the-loop approach, that is, to use AI (artificial intelligence) algorithms that prioritize the physician's decision and explainable artificial intelligence (XAI). The AIDA was structured into five classes. After a number of "geometric parameters" were collected, the data obtained from the AIDA analysis were entered into a red, yellow, or green zone, linked to the analysis of the progress of labor. Using the AIDA analysis, we were able to identify five reference classes for patients in labor, each of which had a certain sort of birth outcome. A 100% cesarean birth prediction was made in two of these five classes. The use of artificial intelligence, through the evaluation of certain obstetric parameters in specific decision-making algorithms, allows physicians to systematically understand how the results of the algorithms can be explained. This approach can be useful in evaluating the progress of labor and predicting the labor outcome, including spontaneous, whether operative VD (vaginal delivery) should be attempted, or if ICD (intrapartum cesarean delivery) is preferable or necessary. In this investigation, we will seek, with a critical eye, to evaluate the implications of some geometric parameters measured using an intrapartum ultrasound trying to highlight all the possible complications and problems of a dystocic and eutocic labor, with an evaluation of a big volume of women in pregnancy and during labor.
Currently, the Artificial Intelligence Dystocia Algorithm (AIDA) represents a significant advancement in the application of artificial intelligence to intrapartum care. Developed through two key studies, AIDA 1 and AIDA 2, this innovative novel approach combines and integrates multiple geometric parameters measured through intrapartum ultrasonography with machine learning techniques and algorithms to assess labor progress and predict delivery outcomes. The selection of algorithms, methodologies for performance evaluation, and the metrics employed were meticulously delineated in the respective publications on AIDA 1 and AIDA 2. These seminal papers provide comprehensive elucidations of the machine learning techniques utilized, the rationale underpinning their selection, and the rigorous performance metrics applied to assess their efficacy in predicting labor outcomes. The high values obtained demonstrate the robust discriminative capability of the algorithms chosen in distinguishing between different delivery outcomes, underscoring the potential clinical utility of the AIDA approach in obstetric decision-making.
The AIDA Methodology A two-step methodology was applied to the data sample. The initial step, the correlation analysis, employed Pearson's correlation coefficient to ascertain that the four geometric parameters exhibited negligible or statistically insignificant correlations, ensuring each parameter contributed unique information regarding labor progression. The subsequent step, the machine learning algorithm selection, involved applying diverse supervised machine learning algorithms to the four geometric parameters in conjunction with physician-determined delivery outcomes. The predictive performance of each algorithm was quantified to identify the most efficacious models.
The AIDA Classification System The classification system is predicated upon the identification of cut-off values for each geometric parameter associated with intrapartum cesarean delivery (ICD) and non-ICD outcomes. A decision tree algorithm was utilized to establish these cut-offs: for each parameter, values strongly associated with non-ICD outcomes were designated as "green", those highly correlated with ICD were classified as "red", and in cases where the data sample revealed intermediate ranges of uncertainty, a "yellow" designation was applied.
Having assigned a color to each individual value for the four geometric parameters for every parturition, the AIDA classification system employs a structured approach to categorizing each labor event into one of five distinct classes. This color-coded stratification of the geometric parameters facilitates a nuanced assessment of labor progression, enabling a more refined classification of each case. AIDA class 0 denotes all four parameters being within the green zone, indicating a high probability of non-ICD outcomes. AIDA class 1 indicates one parameter being in the red or yellow zone and three being in the green zone. AIDA class 2 signifies two parameters being in the red or yellow zone and two being in the green zone. AIDA class 3 represents three parameters being in the red or yellow zone and one being in the green zone. AIDA class 4 denotes all four parameters being within the red or yellow zone, suggesting a heightened likelihood of ICD.
The AIDA's Prediction Performance The integration of the delivery predictions obtained from the three best performing algorithms with the AIDA classification system yielded significantly improved results. A particularly salient finding was the algorithm's high predictive accuracy for delivery outcomes, notably in AIDA classes 0 and 4. In AIDA class 0, characterized by all geometric parameters being within the green zone, the consistent prediction of non-ICD outcomes suggests its potential utility in identifying cases where intervention may be safely deferred. Conversely, the accurate prediction of ICD in AIDA class 4 cases could expedite decision-making for cesarean delivery, potentially optimizing maternal and fetal out-comes by mitigating the duration of prolonged, unproductive labor. The final methodological step entails employing the most effective machine learning algorithms for predicting delivery outcomes based on the four geometric parameters' values with consideration of the relevant AIDA class. This approach enables clinicians to evaluate a pre-diction's clinical reliability.
The remarkable predictive accuracy, particularly at the extremes of the AIDA classification spectrum, underscores the potential of this algorithmic approach to enhance clinical decision-making in labor management.
Potential Advantages of the AIDA in Dystocic Labor Management Dystocic labor, also known as dystocia, broadly defined as difficult or obstructed labor, encompasses a range of conditions that impede the normal progression of labor and delivery as forms of difficult labor characterized by abnormally slow progress.
This condition can arise due to inefficient uterine contractions, abnormal fetal presentation, or other complications that impede the normal process of childbirth. Dystocia can lead to obstructed labor, where despite strong uterine contractions, the fetus cannot descend through the birth canal due to an insurmountable barrier, often occurring at the pelvic brim.
Several types of dystocia have been recognized in obstetrics, each with characteristics and management implications, and the classification may vary slightly depending on the medical literature or clinical approach: geometric dystocia, mechanical dystocia, dynamic dystocia, fetal dystocia, uterine dystocia, functional dystocia, soft tissue dystocia, compound presentations, maternal exhaustion dystocia, and labor dystocia.
Through its simultaneous measurement of four geometric parameters-the angle of progression, asynclitism degree, head-symphysis distance, and midline angle-the AIDA offers a comprehensive view of the spatial relationships between the fetus and the maternal pelvis, provides an objective assessment of the fetal position, and holds potential as a comprehensive tool for evaluating, directly or indirectly, various types of dystocia documented in the medical literature.
This approach provides a more detailed and accurate picture of the progress of labor or labor obstruction that might be missed by traditional assessment methods. By quantifying parameters like the degree of asynclitism, the AIDA may enable earlier detection of fetal malpositions that could lead to dystocia.
The AIDA classification system offers a nuanced approach to risk assessment, helping clinicians tailor their management strategies.
Currently, the Artificial Intelligence Dystocia Algorithm (AIDA) represents a significant advancement in the application of artificial intelligence to intrapartum care. Developed through two key studies, AIDA 1 and AIDA 2, this innovative novel approach combines and integrates multiple geometric parameters measured through intrapartum ultrasonography with machine learning techniques and algorithms to assess labor progress and predict delivery outcomes. The selection of algorithms, methodologies for performance evaluation, and the metrics employed were meticulously delineated in the respective publications on AIDA 1 and AIDA 2. These seminal papers provide comprehensive elucidations of the machine learning techniques utilized, the rationale underpinning their selection, and the rigorous performance metrics applied to assess their efficacy in predicting labor outcomes. The high values obtained demonstrate the robust discriminative capability of the algorithms chosen in distinguishing between different delivery outcomes, underscoring the potential clinical utility of the AIDA approach in obstetric decision-making.
The AIDA Methodology A two-step methodology was applied to the data sample. The initial step, the correlation analysis, employed Pearson's correlation coefficient to ascertain that the four geometric parameters exhibited negligible or statistically insignificant correlations, ensuring each parameter contributed unique information regarding labor progression. The subsequent step, the machine learning algorithm selection, involved applying diverse supervised machine learning algorithms to the four geometric parameters in conjunction with physician-determined delivery outcomes. The predictive performance of each algorithm was quantified to identify the most efficacious models.
The AIDA Classification System The classification system is predicated upon the identification of cut-off values for each geometric parameter associated with intrapartum cesarean delivery (ICD) and non-ICD outcomes. A decision tree algorithm was utilized to establish these cut-offs: for each parameter, values strongly associated with non-ICD outcomes were designated as "green", those highly correlated with ICD were classified as "red", and in cases where the data sample revealed intermediate ranges of uncertainty, a "yellow" designation was applied.
Having assigned a color to each individual value for the four geometric parameters for every parturition, the AIDA classification system employs a structured approach to categorizing each labor event into one of five distinct classes. This color-coded stratification of the geometric parameters facilitates a nuanced assessment of labor progression, enabling a more refined classification of each case. AIDA class 0 denotes all four parameters being within the green zone, indicating a high probability of non-ICD outcomes. AIDA class 1 indicates one parameter being in the red or yellow zone and three being in the green zone. AIDA class 2 signifies two parameters being in the red or yellow zone and two being in the green zone. AIDA class 3 represents three parameters being in the red or yellow zone and one being in the green zone. AIDA class 4 denotes all four parameters being within the red or yellow zone, suggesting a heightened likelihood of ICD.
The AIDA's Prediction Performance The integration of the delivery predictions obtained from the three best performing algorithms with the AIDA classification system yielded significantly improved results. A particularly salient finding was the algorithm's high predictive accuracy for delivery outcomes, notably in AIDA classes 0 and 4. In AIDA class 0, characterized by all geometric parameters being within the green zone, the consistent prediction of non-ICD outcomes suggests its potential utility in identifying cases where intervention may be safely deferred. Conversely, the accurate prediction of ICD in AIDA class 4 cases could expedite decision-making for cesarean delivery, potentially optimizing maternal and fetal out-comes by mitigating the duration of prolonged, unproductive labor. The final methodological step entails employing the most effective machine learning algorithms for predicting delivery outcomes based on the four geometric parameters' values with consideration of the relevant AIDA class. This approach enables clinicians to evaluate a pre-diction's clinical reliability.
The remarkable predictive accuracy, particularly at the extremes of the AIDA classification spectrum, underscores the potential of this algorithmic approach to enhance clinical decision-making in labor management.
Potential Advantages of the AIDA in Dystocic Labor Management Dystocic labor, also known as dystocia, broadly defined as difficult or obstructed labor, encompasses a range of conditions that impede the normal progression of labor and delivery as forms of difficult labor characterized by abnormally slow progress.
This condition can arise due to inefficient uterine contractions, abnormal fetal presentation, or other complications that impede the normal process of childbirth. Dystocia can lead to obstructed labor, where despite strong uterine contractions, the fetus cannot descend through the birth canal due to an insurmountable barrier, often occurring at the pelvic brim.
Several types of dystocia have been recognized in obstetrics, each with characteristics and management implications, and the classification may vary slightly depending on the medical literature or clinical approach: geometric dystocia, mechanical dystocia, dynamic dystocia, fetal dystocia, uterine dystocia, functional dystocia, soft tissue dystocia, compound presentations, maternal exhaustion dystocia, and labor dystocia.
Through its simultaneous measurement of four geometric parameters-the angle of progression, asynclitism degree, head-symphysis distance, and midline angle-the AIDA offers a comprehensive view of the spatial relationships between the fetus and the maternal pelvis, provides an objective assessment of the fetal position, and holds potential as a comprehensive tool for evaluating, directly or indirectly, various types of dystocia documented in the medical literature.
This approach provides a more detailed and accurate picture of the progress of labor or labor obstruction that might be missed by traditional assessment methods. By quantifying parameters like the degree of asynclitism, the AIDA may enable earlier detection of fetal malpositions that could lead to dystocia.
The AIDA classification system offers a nuanced approach to risk assessment, helping clinicians tailor their management strategies.
Conditions
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Delivery Complication
Delivery Problem
Delivery; Injury
Delivery; Injury, Maternal
Delivery;Breech;Stillbirth
Delivery Problem for Fetus
Study Design
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Observational Model Type
COHORT
Study Time Perspective
PROSPECTIVE
Study Groups
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Labor
The women will be followed until and after the birth, under intrapartum ultrasound monitoring, with all clinical, ultrasonographic and obstetric parameters, analyzed by AIDA method.
AIDA Analysis
Intervention Type
DEVICE
The women will be followed until and after the birth, under intrapartum ultrasound monitoring, with all clinical, ultrasonographic and obstetric parameters, analyzed by AIDA method.
Interventions
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AIDA Analysis
The women will be followed until and after the birth, under intrapartum ultrasound monitoring, with all clinical, ultrasonographic and obstetric parameters, analyzed by AIDA method.
Intervention Type
DEVICE
Eligibility Criteria
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Inclusion Criteria
All patients in pregnancy, nulliparae, candidates for spontaneous or induced labor, monitored by intrapartum ultrasound, collecting the ultrasound parameters of the labor progress.
1. pregnants in labor, at first pregnancy
2. gestational age ≥37 weeks of gestation
1. pregnants in labor, at first pregnancy
2. gestational age ≥37 weeks of gestation
Exclusion Criteria
1. Patients who are candidates for cesarean section.
2. Patients in premature labor.
3. Patients who do not agree to participate in the study.
5. Missing data relevant for the study.
2. Patients in premature labor.
3. Patients who do not agree to participate in the study.
5. Missing data relevant for the study.
Minimum Eligible Age
20 Years
Maximum Eligible Age
45 Years
Eligible Sex
FEMALE
Accepts Healthy Volunteers
Yes
Sponsors
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Centro di Ricerca Clinica Salentino
NETWORK
Sponsor Role
lead
Responsible Party
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Responsibility Role
SPONSOR
Principal Investigators
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Andrea Tinelli, MD
Role: STUDY_DIRECTOR
Veris delli Ponti Hospital Scorrano, 73020 Lecce, Italy
Locations
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Ospedale Veris delli Ponti
Scorrano, Lecce, Italy
Site Status
RECRUITING
Andrea Tinelli
Lecce, Le, Italy
Site Status
RECRUITING
Countries
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Italy
Central Contacts
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Facility Contacts
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References
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Obstetric care consensus no. 1: safe prevention of the primary cesarean delivery. Obstet Gynecol. 2014 Mar;123(3):693-711. doi: 10.1097/01.AOG.0000444441.04111.1d.
Reference Type
RESULT
Kissler K, Hurt KJ. The Pathophysiology of Labor Dystocia: Theme with Variations. Reprod Sci. 2023 Mar;30(3):729-742. doi: 10.1007/s43032-022-01018-6. Epub 2022 Jul 11.
Reference Type
RESULT
LeFevre NM, Krumm E, Cobb WJ. Labor Dystocia in Nulliparous Women. Am Fam Physician. 2021 Jan 15;103(2):90-96.
Reference Type
RESULT
Akmal S, Paterson-Brown S. Malpositions and malpresentations of the foetal head. Obstet Gynaecol Reprod Med. 2009 Sep 1;19(9):240-6.
Reference Type
RESULT
Malvasi A, Barbera A, Di Vagno G, Gimovsky A, Berghella V, Ghi T, Di Renzo GC, Tinelli A. Asynclitism: a literature review of an often forgotten clinical condition. J Matern Fetal Neonatal Med. 2015 Nov;28(16):1890-4. doi: 10.3109/14767058.2014.972925. Epub 2014 Oct 29.
Reference Type
RESULT
Buchmann EJ, Libhaber E. Sagittal suture overlap in cephalopelvic disproportion: blinded and non-participant assessment. Acta Obstet Gynecol Scand. 2008;87(7):731-7. doi: 10.1080/00016340802179848.
Reference Type
RESULT
Hung CMW, Chan VYT, Ghi T, Lau W. Asynclitism in the second stage of labor: prevalence, associations, and outcome. Am J Obstet Gynecol MFM. 2021 Sep;3(5):100437. doi: 10.1016/j.ajogmf.2021.100437. Epub 2021 Jul 1.
Reference Type
RESULT
Malvasi A, Vinciguerra M, Lamanna B, Cascardi E, Damiani GR, Muzzupapa G, Kosmas I, Beck R, Falagario M, Vimercati A, Cicinelli E, Trojano G, Tinelli A, Cazzato G, Dellino M. Asynclitism and Its Ultrasonographic Rediscovery in Labor Room to Date: A Systematic Review. Diagnostics (Basel). 2022 Nov 30;12(12):2998. doi: 10.3390/diagnostics12122998.
Reference Type
RESULT
Chan VYT, Lau WL. Intrapartum ultrasound and the choice between assisted vaginal and cesarean delivery. Am J Obstet Gynecol MFM. 2021 Nov;3(6S):100439. doi: 10.1016/j.ajogmf.2021.100439. Epub 2021 Jun 30.
Reference Type
RESULT
Malvasi A, Tinelli A, Barbera A, Eggebo TM, Mynbaev OA, Bochicchio M, Pacella E, Di Renzo GC. Occiput posterior position diagnosis: vaginal examination or intrapartum sonography? A clinical review. J Matern Fetal Neonatal Med. 2014 Mar;27(5):520-6. doi: 10.3109/14767058.2013.825598. Epub 2013 Sep 13.
Reference Type
RESULT
Bellussi F, Ghi T, Youssef A, Salsi G, Giorgetta F, Parma D, Simonazzi G, Pilu G. The use of intrapartum ultrasound to diagnose malpositions and cephalic malpresentations. Am J Obstet Gynecol. 2017 Dec;217(6):633-641. doi: 10.1016/j.ajog.2017.07.025. Epub 2017 Jul 22.
Reference Type
RESULT
Skinner SM, Giles-Clark HJ, Higgins C, Mol BW, Rolnik DL. Prognostic accuracy of ultrasound measures of fetal head descent to predict outcome of operative vaginal birth: a comparative systematic review and meta-analysis. Am J Obstet Gynecol. 2023 Jul;229(1):10-22.e10. doi: 10.1016/j.ajog.2022.11.1294. Epub 2022 Nov 23.
Reference Type
RESULT
Gimovsky AC. Defining arrest in the first and second stages of labor. Minerva Obstet Gynecol. 2021 Feb;73(1):6-18. doi: 10.23736/S2724-606X.20.04644-4. Epub 2020 Sep 3.
Reference Type
RESULT
Pergialiotis V, Bellos I, Antsaklis A, Papapanagiotou A, Loutradis D, Daskalakis G. Maternal and neonatal outcomes following a prolonged second stage of labor: A meta-analysis of observational studies. Eur J Obstet Gynecol Reprod Biol. 2020 Sep;252:62-69. doi: 10.1016/j.ejogrb.2020.06.018. Epub 2020 Jun 10.
Reference Type
RESULT
Ben-Haroush A, Melamed N, Kaplan B, Yogev Y. Predictors of failed operative vaginal delivery: a single-center experience. Am J Obstet Gynecol. 2007 Sep;197(3):308.e1-5. doi: 10.1016/j.ajog.2007.06.051.
Reference Type
RESULT
Ghi T, Youssef A, Maroni E, Arcangeli T, De Musso F, Bellussi F, Nanni M, Giorgetta F, Morselli-Labate AM, Iammarino MT, Paccapelo A, Cariello L, Rizzo N, Pilu G. Intrapartum transperineal ultrasound assessment of fetal head progression in active second stage of labor and mode of delivery. Ultrasound Obstet Gynecol. 2013 Apr;41(4):430-5. doi: 10.1002/uog.12379.
Reference Type
RESULT
Ghi T, Eggebo T, Lees C, Kalache K, Rozenberg P, Youssef A, Salomon LJ, Tutschek B. ISUOG Practice Guidelines: intrapartum ultrasound. Ultrasound Obstet Gynecol. 2018 Jul;52(1):128-139. doi: 10.1002/uog.19072.
Reference Type
RESULT
Rizzo G, Ghi T, Henrich W, Tutschek B, Kamel R, Lees CC, Mappa I, Kovalenko M, Lau W, Eggebo T, Achiron R, Sen C. Ultrasound in labor: clinical practice guideline and recommendation by the WAPM-World Association of Perinatal Medicine and the PMF-Perinatal Medicine Foundation. J Perinat Med. 2022 May 27;50(8):1007-1029. doi: 10.1515/jpm-2022-0160. Print 2022 Oct 26.
Reference Type
RESULT
Nassr AA, Berghella V, Hessami K, Bibbo C, Bellussi F, Robinson JN, Marsoosi V, Tabrizi R, Safari-Faramani R, Tolcher MC, Shamshirsaz AA, Clark SL, Belfort MA, Shamshirsaz AA. Intrapartum ultrasound measurement of angle of progression at the onset of the second stage of labor for prediction of spontaneous vaginal delivery in term singleton pregnancies: a systematic review and meta-analysis. Am J Obstet Gynecol. 2022 Feb;226(2):205-214.e2. doi: 10.1016/j.ajog.2021.07.031. Epub 2021 Aug 9.
Reference Type
RESULT
Malvasi A, Malgieri LE, Cicinelli E, Vimercati A, D'Amato A, Dellino M, Trojano G, Difonzo T, Beck R, Tinelli A. Artificial Intelligence, Intrapartum Ultrasound and Dystocic Delivery: AIDA (Artificial Intelligence Dystocia Algorithm), a Promising Helping Decision Support System. J Imaging. 2024 Apr 29;10(5):107. doi: 10.3390/jimaging10050107.
Reference Type
RESULT
Malvasi A, Malgieri LE, Cicinelli E, Vimercati A, Achiron R, Sparic R, D'Amato A, Baldini GM, Dellino M, Trojano G, Beck R, Difonzo T, Tinelli A. AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree. J Imaging. 2024 Aug 9;10(8):194. doi: 10.3390/jimaging10080194.
Reference Type
RESULT
Related Links
Access external resources that provide additional context or updates about the study.
https://link.springer.com/chapter/10.1007/978-3-030-57595-3_22
Malvasi A, Gustapane S, Malvasi M, Vinciguerra M, Tinelli A, Beck R. Semeiotics of Intrapartum Ultrasonography: New Diagnostic Sonographic Sign of Fetal Malpositions and Malrotations.
https://doi.org/10.1007/978-3-031-10067-3_3
Malgieri, L.E. Ontologies, Machine Learning and Deep Learning in Obstetrics. In Practical Guide to Simulation in Delivery Room Emergencies; Cinnella, G., Beck, R., Malvasi, A., Eds.; Springer: Cham, Switzerland, 2023.
Other Identifiers
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CER 0320
Identifier Type: -
Identifier Source: org_study_id