Table 3 Characteristics of studies in terms of ANN modeling and accuracy

Alfieri et al

The gene transcript expression of 190 wound healing, inflammatory, osteogenic, and vascular genes

ROC curve

Better than LASSO LR

AUC = 0.780

Oncogenomics Online ANN Analysis system

Almhdie et al

KL grades, OARSI grades, demographic, WOMAC pain, race, and history of knee injury, and TBT

ROC curve

Better than LR

AUC^OAI = 0.75; AUC^MOST = 0.81

ImageNet

Bevevino et al

Demographics, mechanism of injury, wound size and location, fracture types, interval and definitive treatment procedures, rotational or free tissue transfer, skin graft, and neurovascular procedures, and ipsilateral and contralateral orthopaedic injuries

ROC curve

Better than LR and random forest model

AUC = 0.800 (95% CI: 0.770, 0.820)

SAS

Bowman et al

Demographic, clinical and

neurophysiological variables^a

ROC curve

Better than LR

AUC = 0.767

MATLAB

Chen et al

Demographic, referral to lower-level medical institutions, urbanization, socioeconomic status, number of comorbidities, intracapsular fracture, hospital level, hospital volume, and surgeon volume

ROC curve

Better than Cox regression model

AUC = 0.930 (95% CI: 0.900, 0.960)

STATISTICA

Eller-Vainicher et al

Menopausal age, number of pregnancies, breast feeding, smoking habits, alcohol consumption, previous clinical fragility fractures at spine, ribs, wrist and hip, BMI, calcium intake, co-morbidities^b

ROC curve

Better than LR

AUC^SDI≥1 = 0.714; AUC^SDI≥5 = 0.823

TWIST system

Jalali et al

Demographics, intra/postoperative transfusion, intraoperative surgical and anesthetic management, postoperative management and laboratory results, the occurrence of prespecified intraoperative and postoperative complications, and the length of intensive care unit and hospital stay in calendar days

ROC curve

Inferior than GBM

AUC = 0.790;

Python

Kim et al. (Korea)

T2-sagittal consecutive lumbar spine MR images, T2-weighted sagittal lumbar spine MR slices, MR images obtained prior to the TFESI

ROC curve

-

AUC = 0.827(95% CI, 0.774,0.909)

Python

Kim et al. (USA)

Demographic, diabetes, smoking, steroid use, coagulopathy, functional status, ASA class O3, BMI, pulmonary comorbidities, and cardiac comorbidities

ROC curve

Better than LR

AUC^cardiac = 0.768; AUC^VTE = 0.542; AUC^wound = 0.606; AUC^mortality = 0.844

MATLAB

Lu et al

Age, sex, body mass index, type of sports participation, clinically documented ligamentous laxity, clinical history of instability, radiographic findings, management, recurrent instability, and development of clinically symptomatic osteoarthritis

ROC curve

Inferior than XGBoost

AUC^recurrence = 0.823 (95% CI, 0.821,0.824); AUC^surgery = 0.689 (95% CI, 0.687,0.692); AUC^{osteoarthritis} = 0.692 (95% CI, 0.687,0.697)

R

Miyoshi et al

Demographic, ESR,

TEN, ALB, MONO, RBC, PSL, MTX, HbA1c and Pre bio

ROC curve

-

AUC = 0.750

WEKA software

package

Norgeot et al

Prior CDAI score, ESR and CRP level, DMARDs, oral and injected glucocorticoids, autoantibodies, age, sex, and race/ethnicity

ROC curve

-

AUC^UH = 0.910 (95% CI, 0.860,0.960); AUC^SNH = 0.740 (95% CI, 0.650,0.830)

Github

Salgueiro et al

MPQ, the HAQ-DI, and the anxiety subscale of HADS

ROC curve

Better than LR

AUC^ANN1 = 0.917; AUC^ANN2 = 0.947

-

Scheer et al

Demographic, radiographic, and surgical factors

ROC curve

-

AUC = 0.890

SPSS Modeler

Shin et al

Changes in clinical symptoms and blood inflammatory markers

ROC curve

-

AUC = 0.902 (95%CI, 0.804, 0.999)

Keras and TensorFlow

Su et al

Sex, age, BMI, region, morning stiffness time, step count, and osteophyte area

ROC curve

-

AUC^worse = 0.814; AUC^unchanged = 0.700; AUC^improved = 0.761

R

Wang et al

Age, EO, PLT, WBC, NEUT, U-SG, U-WBC, U-WBC

ROC curve

-

AUC^ILD = 0.792; AUC^PF = 0.751

STATISTICA

Yahara et al

Frontal view of the total spine radiographs: the C7 vertebra and diaphragm; diaphragm and ilium; and C7 vertebra and ilium

ROC curve

-

AUC = 0.700

MATLAB