Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

doi:10.1038/s41598-022-07139-x

Clinical Trial

. 2022 Feb 24;12(1):3138.

doi: 10.1038/s41598-022-07139-x.

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

Hyun Iee Shin¹, Hyung-Ik Shin², Moon Suk Bang², Don-Kyu Kim¹, Seung Han Shin³, Ee-Kyung Kim³, Yoo-Jin Kim⁴, Eun Sun Lee^{3

5}, Seul Gi Park³, Hye Min Ji², Woo Hyung Lee⁶

Affiliations

¹ Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
² Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
³ Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
⁴ Department of Pediatrics, Chungbuk National University Hospital, Cheongju, Republic of Korea.
⁵ Department of Pediatrics, Chung-Ang University Hospital, Seoul, Republic of Korea.
⁶ Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. whlee909@snu.ac.kr.

PMID: 35210507
PMCID: PMC8873498
DOI: 10.1038/s41598-022-07139-x

Clinical Trial

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

Hyun Iee Shin et al. Sci Rep. 2022.

. 2022 Feb 24;12(1):3138.

doi: 10.1038/s41598-022-07139-x.

Authors

Hyun Iee Shin¹, Hyung-Ik Shin², Moon Suk Bang², Don-Kyu Kim¹, Seung Han Shin³, Ee-Kyung Kim³, Yoo-Jin Kim⁴, Eun Sun Lee^{3

5}, Seul Gi Park³, Hye Min Ji², Woo Hyung Lee⁶

Affiliations

¹ Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
² Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
³ Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
⁴ Department of Pediatrics, Chungbuk National University Hospital, Cheongju, Republic of Korea.
⁵ Department of Pediatrics, Chung-Ang University Hospital, Seoul, Republic of Korea.
⁶ Department of Rehabilitation Medicine, Seoul National University Children's Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. whlee909@snu.ac.kr.

PMID: 35210507
PMCID: PMC8873498
DOI: 10.1038/s41598-022-07139-x

Abstract

This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The Hammersmith Infant Neurological Examination (HINE) was performed in infants at 4 months of corrected age. Joint positional data were extracted using a pretrained pose-estimation model. Complexity and similarity indices of joint angle and angular velocity in terms of sample entropy and Pearson correlation coefficient were compared between the infants with HINE < 60 and ≥ 60. Video images of spontaneous movements were recorded in 65 preterm infants at term-equivalent age. Complexity indices of joint angles and angular velocities differed between the infants with HINE < 60 and ≥ 60 and correlated positively with HINE scores in most of the joints at the upper and lower extremities (p < 0.05). Similarity indices between each joint angle or joint angular velocity did not differ between the two groups in most of the joints at the upper and lower extremities. Quantitative assessments of spontaneous movements in preterm infants are feasible using a deep learning algorithm and sample entropy. The results indicated that complexity indices of joint movements at both the upper and lower extremities can be potential candidates for detecting developmental outcomes in preterm infants.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
Overall processes of acquisition of joint angles and joint angular velocities at the upper and lower extremities from video images of spontaneous movements in preterm infants. S shoulder, E elbow, W wrist, H hip, K knee, A ankle.

**Figure 2**
Illustrations of joint angles and joint angular velocities at the bilateral shoulders, elbows, hips, and knees in two preterm infants with Hammersmith Infant Neurological Examination (HINE) ≥ 60 (A,B) and those with HINE < 60 (C,D).

See this image and copyright information in PMC

Cited by

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.
Groos D, Adde L, Aubert S, Boswell L, de Regnier RA, Fjørtoft T, Gaebler-Spira D, Haukeland A, Loennecken M, Msall M, Möinichen UI, Pascal A, Peyton C, Ramampiaro H, Schreiber MD, Silberg IE, Songstad NT, Thomas N, Van den Broeck C, Øberg GK, Ihlen EAF, Støen R. Groos D, et al. JAMA Netw Open. 2022 Jul 1;5(7):e2221325. doi: 10.1001/jamanetworkopen.2022.21325. JAMA Netw Open. 2022. PMID: 35816301 Free PMC article. Clinical Trial.
Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study.
Kim Y, Kim H, Choi J, Cho K, Yoo D, Lee Y, Park SJ, Jeong MH, Jeong SH, Park KH, Byun SY, Kim T, Ahn SH, Cho WH, Lee N. Kim Y, et al. BMC Pediatr. 2023 Oct 23;23(1):525. doi: 10.1186/s12887-023-04350-1. BMC Pediatr. 2023. PMID: 37872515 Free PMC article.
Deep-learning-based markerless tracking of distal anatomical landmarks in clinically recorded videos for assessing infant movement patterns associated with neurodevelopmental status.
Abbasi H, Mollet SR, Williams SA, Battin MR, Besier TF, McMorland AJC. Abbasi H, et al. J R Soc N Z. 2023 Oct 25;55(2):223-240. doi: 10.1080/03036758.2023.2269095. eCollection 2025. J R Soc N Z. 2023. PMID: 39677381 Free PMC article.
A systematic review of portable technologies for the early assessment of motor development in infants.
Deng W, O'Brien MK, Andersen RA, Rai R, Jones E, Jayaraman A. Deng W, et al. NPJ Digit Med. 2025 Jan 27;8(1):63. doi: 10.1038/s41746-025-01450-3. NPJ Digit Med. 2025. PMID: 39870826 Free PMC article.
Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders.
Bruschetta R, Caruso A, Micai M, Campisi S, Tartarisco G, Pioggia G, Scattoni ML. Bruschetta R, et al. Diagnostics (Basel). 2025 Jan 8;15(2):136. doi: 10.3390/diagnostics15020136. Diagnostics (Basel). 2025. PMID: 39857020 Free PMC article.

See all "Cited by" articles

References

1. Novak I, et al. Early, accurate diagnosis and early intervention in cerebral palsy: Advances in diagnosis and treatment. JAMA Pediatr. 2017;171:897–907. doi: 10.1001/jamapediatrics.2017.1689. - DOI - PMC - PubMed
1. Einspieler C, et al. Cerebral palsy: Early markers of clinical phenotype and functional outcome. J. Clin. Med. 2019;8:1616. doi: 10.3390/jcm8101616. - DOI - PMC - PubMed
1. King AR, et al. Early detection of cerebral palsy in high-risk infants: Translation of evidence into practice in an Australian hospital. J. Paediatr. Child Health. 2021;57:246–250. doi: 10.1111/jpc.15191. - DOI - PubMed
1. Morgan C, et al. The pooled diagnostic accuracy of neuroimaging, general movements, and neurological examination for diagnosing cerebral palsy early in high-risk infants: A case control study. J. Clin. Med. 2019;8:1879. doi: 10.3390/jcm8111879. - DOI - PMC - PubMed
1. Prechtl HF, et al. An early marker for neurological deficits after perinatal brain lesions. Lancet. 1997;349:1361–1363. doi: 10.1016/S0140-6736(96)10182-3. - DOI - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

LinkOut - more resources

Full Text Sources

[1] Novak I, et al. Early, accurate diagnosis and early intervention in cerebral palsy: Advances in diagnosis and treatment. JAMA Pediatr. 2017;171:897–907. doi: 10.1001/jamapediatrics.2017.1689. - DOI - PMC - PubMed

[2] Novak I, et al. Early, accurate diagnosis and early intervention in cerebral palsy: Advances in diagnosis and treatment. JAMA Pediatr. 2017;171:897–907. doi: 10.1001/jamapediatrics.2017.1689. - DOI - PMC - PubMed

[3] Einspieler C, et al. Cerebral palsy: Early markers of clinical phenotype and functional outcome. J. Clin. Med. 2019;8:1616. doi: 10.3390/jcm8101616. - DOI - PMC - PubMed

[4] Einspieler C, et al. Cerebral palsy: Early markers of clinical phenotype and functional outcome. J. Clin. Med. 2019;8:1616. doi: 10.3390/jcm8101616. - DOI - PMC - PubMed

[5] King AR, et al. Early detection of cerebral palsy in high-risk infants: Translation of evidence into practice in an Australian hospital. J. Paediatr. Child Health. 2021;57:246–250. doi: 10.1111/jpc.15191. - DOI - PubMed

[6] King AR, et al. Early detection of cerebral palsy in high-risk infants: Translation of evidence into practice in an Australian hospital. J. Paediatr. Child Health. 2021;57:246–250. doi: 10.1111/jpc.15191. - DOI - PubMed

[7] Morgan C, et al. The pooled diagnostic accuracy of neuroimaging, general movements, and neurological examination for diagnosing cerebral palsy early in high-risk infants: A case control study. J. Clin. Med. 2019;8:1879. doi: 10.3390/jcm8111879. - DOI - PMC - PubMed

[8] Morgan C, et al. The pooled diagnostic accuracy of neuroimaging, general movements, and neurological examination for diagnosing cerebral palsy early in high-risk infants: A case control study. J. Clin. Med. 2019;8:1879. doi: 10.3390/jcm8111879. - DOI - PMC - PubMed

[9] Prechtl HF, et al. An early marker for neurological deficits after perinatal brain lesions. Lancet. 1997;349:1361–1363. doi: 10.1016/S0140-6736(96)10182-3. - DOI - PubMed

[10] Prechtl HF, et al. An early marker for neurological deficits after perinatal brain lesions. Lancet. 1997;349:1361–1363. doi: 10.1016/S0140-6736(96)10182-3. - DOI - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

Affiliations

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources