Hill–Sachs lesion measurement with tridimensional models in anterior shoulder instability

Miyazaki, Alberto Naoki; Silva, Luciana Andrade; Santos, Pedro Doneux; Sella, Guilherme do Val; Nagaya, Leonardo Hideto; Checchia, Sergio Luiz

doi:10.1016/j.rboe.2018.03.008

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Revista Brasileira de Ortopedia

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Sumário

Artigos Originais • Rev. bras. ortop. 53 (3) • May-Jun 2018 • https://doi.org/10.1016/j.rboe.2018.03.008 copy

Hill–Sachs lesion measurement with tridimensional models in anterior shoulder instability^☆ ☆ Study conducted at Faculdade de Ciências Médicas da Santa Casa de São Paulo, Departamento de Ortopedia e Traumatologia, São Paulo, SP, with support of the Instituto Renato Archer, Campinas, SP, Brazil.

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Objective

To evaluate the reproducibility and repeatability of Hill–Sachs lesion measurement from computed tomography images, with computer software and tridimensional prototype.

Methods

Three-dimensional models were made from computed tomography images from 14 patients with anterior shoulder instability, using InVesalius 3.0^® software. Hill–Sachs lesions were measured with Rhinocerus 5.0^® software with pre-determined position. Mid-lateral distance, perpendicular to humeral shaft, cranial-caudal distance, parallel to humeral shaft, and the longitudinal distance of the lesion were measured. Using the Printer-ZP 310 three-dimensional printer, plaster models were made. To measure the Hill–Sachs lesion, a calibrated universal digital caliper was used in the same way as the software.

Results

There was intra-observer and inter-observer variability for measurement of the same model. Observers did not perform the measurements in a similar way, showing difficulty to use the method (p < 0.05). Using the software to measure the mid-lateral distance, as well as in the measurement with the caliper, the model type influenced the measurements for each of the observers, rendering the method invalid (p < 0.05).

Conclusion

There was no reproducibility and repeatability for Hill–Sachs lesion measurement between plaster models and software models.

Keywords
Shoulder dislocation; Shoulder joint; Joint instability; Printing, three-dimensional printing; X-ray computed tomography

Round	Model	ML computer	CC computer	D computer	ML model	CC model	D model
1	1	28.51	17.27	27.06	28.4	18.04	26.78
1	2	21.17	26.61	35.7	17.81	29.9	38.4
1	3	14.37	24.57	24.23	18.78	20.45	28.03
1	4	27.23	31.17	35.2	25.43	34.98	40.32
1	5	20.51	26.54	31.03	17.12	31.05	32.51
1	6	23.29	27	33.49	23.4	29.97	31.55
1	7	21.17	25.89	25.85	9.04	18.51	15.72
1	8	20.46	32.04	32.6	20.68	37.06	40.98
1	9	14.45	27.61	26	12.39	25.81	31.85
1	10	18.6	33.19	36.11	15.84	31.24	37.97
1	11	18.6	27.75	28.41	17.31	35.35	40.44
1	12	20.31	28.61	26.36	5	12.23	20.26
1	13	12.51	17.03	16.18	7.77	19.01	22.27
1	14	19.6	35.5	36.59	16.71	33.21	34.52
2	1	28.97	18.42	28.02	22.82	24.28	29.52
2	2	20.03	27.75	34.72	18.33	28.43	39.78
2	3	17.27	22.94	24.5	19.93	24.07	28.33
2	4	26.77	31.98	34.94	24.86	34.83	37.96
2	5	23.64	27.69	30.22	8.39	16.44	16.57
2	6	23.87	27.93	34.26	22.57	28.18	38.22
2	7	10.59	21.03	20.27	6.95	17.47	15.87
2	8	20.49	25.08	28.47	20.3	19.03	39.06
2	9	15.31	28.32	33.27	8.35	16.86	15.73
2	10	16.59	30.47	37.1	16	33.3	36.23
2	11	18.45	25.03	26.06	11.87	29.99	16.03
2	12	18.02	28.32	30.34	2.69	13.98	16.3
2	13	11.59	9.44	9.01	7.4	19.35	21.11
2	14	18.6	34.19	33.41	20.62	34.98	32.51
3	1	27.2	18.97	26.54	28.25	23.63	28.35
3	2	19.43	27.79	34.84	17.16	33.66	37.56
3	3	19.49	22.51	22.89	21.43	13.76	13.64
3	4	24.81	30.02	34.22	25.61	35.34	37.68
3	5	16.36	25.33	28.15	16.45	26.11	27.68
3	6	22.51	25.43	27.06	22.64	27.1	30.66
3	7	10.68	20.84	19.69	9.37	17.98	17.7
3	8	21.62	31.52	29.92	22.11	36.47	40.45
3	9	16.08	27.14	29.14	14.15	30.33	31.95
3	10	17.76	29.85	35.76	16.84	28.88	33.93
3	11	10.17	27.54	28.54	12.22	29.95	15.48
3	12	15.4	16.73	18.39	4.75	15.27	21.06
3	13	10.53	17.3	18.7	6.23	21.4	23.67
3	14	17.89	33.2	33.31	16.94	32.7	50.64

Round	Model	ML computer	CC computer	D computer	ML model	CC model	D model
1	1	26.67	23.67	27.72	26.52	21.41	29.21
1	2	30.18	33.76	36.08	19.9	33.7	39.55
1	3	16.02	23.89	25.22	23.59	23.81	28.83
1	4	27.75	34.62	38.48	29.49	36.53	37.99
1	5	18.37	30.91	33.64	23.59	35.9	38.25
1	6	13.6	22.55	26.68	27.49	31.36	36.48
1	7	14.61	23.33	25.28	13.13	20.04	13.15
1	8	21.17	27.47	27.8	25.21	32.97	40.45
1	9	27.75	34.62	38.48	16.83	30.91	32.93
1	10	18.88	31.76	37.34	18.46	32.55	38.42
1	11	11.3	26.49	28.5	16.03	30.3	32.9
1	12	26.38	34.1	31.83	9	16.54	21.56
1	13	12.72	14.66	16.69	16.66	20.27	28.98
1	14	21.59	40.15	42.44	19.9	35.85	39.62
2	1	28.16	21.55	27.76	24.63	27.87	32.82
2	2	21.46	33.9	35.88	18.74	26.33	37.75
2	3	22.36	30.48	31.5	28.13	22.49	30.63
2	4	27.23	34.3	36.48	28.76	37.79	39.11
2	5	18.19	26.14	34.7	22.23	32.38	39.08
2	6	25.03	34.07	38.77	26.81	31.83	35.23
2	7	19.31	29.61	28.6	13.51	26.26	27.84
2	8	23.14	33.38	31.2	32.19	40.4	41.88
2	9	24.91	38.93	42.19	17.13	27.17	31.36
2	10	28.9	37.19	41.66	19.97	32.47	37.69
2	11	26.54	40.56	42.88	15.22	26.8	33.89
2	12	22.43	35.32	33.06	16.24	24.69	26.55
2	13	14.31	13.16	11.9	20.73	18.99	26.76
2	14	18.75	35.54	36.14	18.81	35.65	39.92
3	1	15.22	25.85	28.27	25.15	29.35	23.2
3	2	19.56	30.88	33.33	18	27.16	36.36
3	3	17.09	24.49	31.21	24.69	24.66	29.11
3	4	26.99	30.85	34.88	28.14	38.93	40.01
3	5	18.03	26.68	31.37	19.79	35.26	33.53
3	6	25.54	30.12	32.71	26.81	34.01	29.91
3	7	12.48	17.11	17.68	12.52	19.83	21.27
3	8	22.52	33.71	32.07	31.87	41.68	37.03
3	9	18.14	33.33	38.9	17.98	33.27	29.34
3	10	22.78	35.77	38.36	17.8	23.81	18.36
3	11	17.37	30.88	32	13.42	32.74	31.13
3	12	23.16	33.58	33.67	7.54	22.5	12.28
3	13	16.68	25.54	28.02	15.53	37.77	29.46
3	14	19.43	35.13	37.53	19.88	27.72	34.02

Round	Model	ML computer	CC computer	D computer	ML model	CC model	D model
1	1	15.41	30.71	33.43	25.44	27.02	32.27
1	2	22.75	31.76	36.64	21.45	32.99	39.81
1	3	20.74	30.94	29.75	21.27	28.4	35.27
1	4	28.39	34.41	37.9	28.44	36.08	40.14
1	5	23.64	32.1	34.74	21.09	33.14	37.32
1	6	27.35	32.91	37.2	27.3	31.81	39.1
1	7	13.88	26.32	28.19	10.45	22.6	23.5
1	8	22.61	33.73	37.2	25.83	38.03	40.52
1	9	15.41	33.72	37.14	18.11	34.06	35.05
1	10	22.6	33.62	37.25	21.68	37.71	40.56
1	11	18.31	33.62	34.52	11.15	28.19	32.73
1	12	14.61	34.67	35.59	10.16	16.38	20.08
1	13	10.8	15.3	17.26	8.95	19.23	23.93
1	14	20.84	35.68	38.94	24.11	36.05	40.79
2	1	17.15	32.1	35.02	18.3	23.71	28.33
2	2	20.86	31.29	34.36	21.76	36.39	40.03
2	3	16.11	33.95	34.21	21.89	24.58	27.89
2	4	26.3	34.53	34.13	28.9	33.85	37.93
2	5	22.02	35.92	37.23	21.78	30.93	35.87
2	6	29.47	31.76	37.62	24.04	30.43	35.63
2	7	12.01	20.31	21.75	9.52	18	17.89
2	8	22.08	38.32	36.15	22.62	36.07	40.2
2	9	14.66	37.44	41.38	14.53	31.65	34.62
2	10	20.46	36.48	39.49	16.05	30.73	33.12
2	11	11.44	32.9	31.93	14.15	29.79	34.36
2	12	10.44	34.76	39.74	6.96	17.44	19.71
2	13	8.69	21.67	30.02	9.55	21.66	21.55
2	14	20.06	40.34	41.65	20.59	32.14	39.46
3	1	30.12	20.74	29.88	22.33	21.06	26.23
3	2	20.33	35.13	34.35	18.58	32.73	34.9
3	3	15.7	27.54	28.2	22.23	27.08	27.82
3	4	28.66	31.06	34.89	24.56	33.86	35.69
3	5	26.99	33.98	40.19	20.58	33.3	34.74
3	6	27.52	31.68	32.07	24.53	28.42	37.71
3	7	12.22	27.02	27.89	7.56	15.55	15.84
3	8	22	35.13	34.35	19.05	36.66	38.55
3	9	16.21	32.68	40.46	11.98	31.61	32.4
3	10	19.63	33.45	36.34	19.14	31.57	36.42
3	11	13.9	29.72	30.65	12.04	28.46	35.09
3	12	7.15	17	18.19	5.15	12.65	15.51
3	13	9.01	12.48	16.47	13.36	22.63	23.21
3	14	18.14	35.39	37.01	21.5	35.59	38.8

	Model	Operator	Operator-model interaction	Contribution of the variability	Distinct number of categories
ML computer	p < 0.05^a a ANOVA.	p > 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	54.9%	1
CC computer	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	p > 0.05^a a ANOVA.	43.98%	1
D computer	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	p > 0.05^a a ANOVA.	41.43%	1
ML model	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	25.43%	1
CC model	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	p > 0.05^a a ANOVA.	36.1%	2
D model	p < 0.05^a a ANOVA.	p < 0.05^a a ANOVA.	p > 0.05^a a ANOVA.	34.90%	1

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E-mail: rbo@sbot.org.br

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[1] *Corresponding author. E-mails: lucalu@terra.com.br, ombro@ombro.med.br (L.A. Silva).